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