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Distributed Scheduling of a Network of Adjustable
Range Sensors for Coverage Problems
Akshaye Dhawan, Ursinus CollegeAung 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
•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
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…
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.
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
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
batteryShown to be NP-Hard in the literature.
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
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 si that
participate in (Cm,tm) we need to associate a range ri with each si
• Makes the problem more interesting because as range increases, target coverage increases but so does energy
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
ALBP
• Adjustable Range Load Balancing Protocol (ALBP)
• States for each sensor
ALBP
Transition Rules:
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,
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:
ADEEPS
Time Complexity
• ALBP: Time complexity is• • Message complexity is • ADEEPS: Time complexity is • Message complexity is (2-hop)
Results
• Lifetime with 25 targets, linear energy model, 30m range
Results
• Lifetime with 25 targets, quadratic energy model, 30m range
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