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Distributed Scheduling of a Network of Adjustable Range Sensors for Coverage Problems

Distributed Scheduling of a Network of Adjustable Range Sensors for Coverage Problems. Akshaye Dhawan, Ursinus College Aung Aung and Sushil K. Prasad Georgia State University. Introduction. Sensor Networks – Consist of a large number of low cost sensor nodes connected to one or more sinks.

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Distributed Scheduling of a Network of Adjustable Range Sensors for Coverage Problems

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  1. Distributed Scheduling of a Network of AdjustableRange Sensors for Coverage Problems Akshaye Dhawan, Ursinus College AungAungand Sushil K. Prasad Georgia State University

  2. Introduction • Sensor Networks – Consist of a large number of low cost sensor nodes connected to one or more sinks

  3. Deployed randomly in and around the phenomenon • Dense networks with many sensors (hundreds-tens of thousands) • Prone to unpredictable failures since they are usually deployed in harsh environments

  4. So what are these useful for? Environmental: Disaster monitoring, Early warning systems (Forest Fires, Tides) Infrastructure: contaminant flow monitoring, structural monitoring Military: Command and control, surveillance, intrusion detection etc. And many more applications… Health Care, Smart Grids, Inventory Management…

  5. Energy • Biggest constraint – energy. • Limited, non-replaceable battery. • Etransmit>Ereceive>=Eidle>>> Esense • Very low power sleep state exists • Energy-efficiency at every layer of the network stack is needed.

  6. Target Coverage • We consider the problem of Target Coverage –at least one sensor always covers each member of a set of targets • Equivalent to area coverage • Dense deployment means overlap in the monitoring regions of sensors • Big idea: Only a subset of these sensors are needed at any given time to cover all targets – called a cover set

  7. The Max. Lifetime Target Coverage Problem Given a regionR, a set of sensors s, a set of targets T. Find a monitoring schedule for these sensors such that: • The total time of the schedule is maximized • All targets are constantly monitored • No sensor is in the schedule for longer than its initial battery Shown to be NP-Hard in the literature.

  8. Scheduling • If we use one active subset – its members die • Idea: Scheduling process to shuffle the active set’s members • Problem: Determine how long to use a set and which set to use next • For an arbitrarily large network – Exponential number of cover sets to choose from • Several centralized and distributed algorithms in the literature – all assume a fixed communication/sensing range for a sensor

  9. Adjustable range model • Now lets make things more interesting… • Adjustable range – Each sensor can vary its range from 0 (off) to MAXDIST • So in addition to picking the sensors sithat participate in (Cm,tm) we need to associate a range riwith each si • Makes the problem more interesting because as range increases, target coverage increases but so does energy

  10. Contributions • Problem studied first by Wu, Cardeiet al • We propose a different adjustable model • Smooth sensing range model in place of discrete range model • Can handle non-uniform battery at each sensor • Present distributed algorithms for maximum lifetime scheduling – 20% lifetime improvement over non-adjustable counterparts

  11. ALBP • Adjustable Range Load Balancing Protocol (ALBP) • Statesfor each sensor

  12. ALBP Transition Rules:

  13. ADEEPS • Intuition: Minimize energy consumption of energy-poor targets • Lifetime of a sensor with battery b, range r and using an energy model e be denoted as Lt(b, r, e). • Maximum lifetime of a target • Lt(b1, r1, e1)+Lt(b2, r2, e2)+Lt(b3, r3, e3)+ … assuming that it can be covered by some sensor with battery bi at distance ri for i = 1, 2,

  14. ADEEPS • Sink: A target t which is the poorest (least total energy of covering sensors) for at least one sensor • Hill: Not the poorest for any covering sensor • Each target has an in-charge sensor:

  15. ADEEPS

  16. Time Complexity • ALBP: Time complexity is • Message complexity is • ADEEPS: Time complexity is • Message complexity is (2-hop)

  17. Results • Lifetime with 25 targets, linear energy model, 30m range

  18. Results • Lifetime with 25 targets, quadratic energy model, 30m range

  19. Conclusion • Show significant lifetime gains by moving to an adjustable sensing model • First distributed scheduling algorithms in this model • 10-20% in a linear model • 35-40% in a quadratic model

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