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Distributed Scheduling of Adjustable Range Sensors for Enhanced Target Coverage

This paper addresses the challenges of maximizing the operational lifetime of sensor networks tasked with target coverage using adjustable sensing ranges. Given the unpredictability of sensor failures and limited battery life, we propose a distributed scheduling algorithm that optimally selects which sensors to activate and at what sensing range. Our approach introduces the Adjustable Range Load Balancing Protocol (ALBP) and demonstrates improvements in lifetime by 20% to 40% compared to traditional methods. This research has implications for environmental monitoring, military applications, and energy-efficient smart systems.

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Distributed Scheduling of Adjustable Range Sensors for Enhanced Target Coverage

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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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