1. 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

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? Infrastructure: contaminant flow monitoring, structural monitoring Environmental: Disaster monitoring, Early warning systems (Forest Fires, Tides) 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. E transmit >E receive >=E idle >>> E sense 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 region R, 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 s i that participate in (C m,t m ) we need to associate a range r i with each s i Makes the problem more interesting because as range increases, target coverage increases but so does energy

10. Contributions Problem studied first by Wu, Cardei et 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) States for 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(b 1, r 1, e 1 )+Lt(b 2, r 2, e 2 )+Lt(b 3, r 3, e 3 )+ … assuming that it can be covered by some sensor with battery b i at distance r i 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