INTRUSION K-COVERAGE IN WIRELESS SENSOR

NETWORKS

Amir Yahyavi

ECE Department, University of Tehran, North Kargar, Tehran, Iran

Laleh Roostapour, Roohollah Aslanzadeh, Mehdi Dehghan

CEIT Department, Amirkabir University of Technology, Tehran, Iran

Nasser Yazdani

ECE Department, University of Tehran, North Kargar, Tehran, Iran

Keywords: k-Coverage, Wireless Sensor networks, Surveillance and Monitoring, Sleep Scheduling.

Abstract: One of the main applications of Wireless Sensor Networks is surveillance and monitoring. Providing and

maintaining the required coverage over the area of an intrusion (or other events of our choice) is of great

importance. The network should be able to provide different levels of coverage based on application needs

and reconfigure itself while ensuring energy efficiency. In this paper we present a dynamic approach to

provide asymptotic k-coverage over the area of an intrusion. This is a probabilistic approach which creates

full coverage over the surveillance zone and provides k-coverage over the area of an event. Our simulations

show that this approach is able to provide the requested coverage while consuming less than a third of the

static approaches. Also due to probabilistic nature of this approach communication overhead is much lower

than deterministic methods.

1 INTRODUCTION

Wireless Sensor Networks usually consist of a large

number of small sensor nodes with limited energy

source which use a low-bandwidth wireless radio for

communication. A sensor node can only last 100-

120 hours on a pair of AA batteries in the active

mode and battery capabilities are only doubled every

35 years(Ye et al., 2002). This makes energy

efficiency the main challenge in application design

for sensor networks.

Putting sensors to periodic sleep in dense sensor

networks has been suggested as a way to increase

the network longevity. (Wang and Xiao, 2005)

Sensor nodes in the sleep mode consume only 0.1%

of the energy consumed in the active mode (Kumar

et al., 2006). Low duty cycle results in higher delay,

lower coverage and connectivity in exchange for

power efficiency. Nodes in the sleep mode are

unable to detect events in their sensing range and are

unable to receive or forward any packets (MAC

layer can have a different duty cycle from the

sensing device which we will discuss later).

Several scheduling schemes have been suggested

to minimize the effect of sleeping nodes on the

desired parameter (delay, connectivity, etc) in the

network (Lu et al., 2005), (Wang et al., 2003).

Coverage is one of the important parameters that are

affected by the scheduling scheme.

In surveillance and monitoring applications, it is

usually required to have at least k sensors cover

every point in the surveillance zone (k-Coverage). In

dense networks (Ye et al., 2002) where there are

more than k sensors present in each area, sensor

nodes are put to low duty cycle. This raises the

question of which nodes should be active in each

cycle in order to maintain the same coverage

(Kumar et al., 2006), (Abrams et al., 2004) while

other nodes in the area go to sleep. It means that we

need a coordination function (Chen et al., 2002)

between neighbor nodes to determine the state of

each node in each cycle in a way that the total

Yahyavi A., Roostapour L., Aslanzadeh R., Dehghan M. and Yazdani N. (2008).

INTRUSION K-COVERAGE IN WIRELESS SENSOR NETWORKS.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 41-46

DOI: 10.5220/0002024900410046

 SciTePress

number of sensors to cover the neighborhood is

approximately k.

The need for a mechanism to dynamically

configure the coverage provided according to the

needs of the application was mentioned in (Wang et

al., 2003). Dynamic configuration of sensor network

helps the network to adapt to different applications’

requirements and maximizes the energy efficiency.

2 RELATED WORKS

(Tian and Georganas, 2002) present a deterministic

method for providing coverage on the surveillance

zone. This method guarantees that the original

sensing coverage is maintained after the redundant

nodes are turned off. This approach assumes that all

nodes have it’s and it’s neighbors location

information. It also requires techniques to estimate

the direction of the received signal which may

require more than one antenna.

(Wang Rui et al., 2006) presents an Ant Colony

method for self organization of sensor networks.

Each sensor is regarded as an immobile ant. Each

ant at each cycle wakes up with probability . Upon

detection of an event the ant lays pheromone which

is diffused to its immediate neighbours. Neighbour

ants that receive this pheromone will increase the

probability of waking up at the next cycle. Based on

the accumulated amount of received pheromone

each node calculates the probability of going to

sleep in the next cycle. This method increases the

percentage of useful nodes (nodes that have detected

an event) in the network. This approach doesn’t

provide guaranteed detection of events.

Most deterministic methods such as (Wang et al.,

2003), (Tian and Georganas, 2002), (Ye et al., 2002)

use an eligibility rule to turn off the redundant nodes

in the area. In order to determine which nodes can be

turned off they either require the location

information of their neighbors or they need to probe

the area for other active sensors. This imposes a

higher communication and computation overhead in

comparison to probabilistic approaches.

In (Kumar et al., 2006) boundary conditions to

have k-coverage in a mostly sleepy network in three

distributions (Grid, Random Uniform, Poisson) are

presented. We use the equations presented in

(Kumar et al., 2006) to dynamically calculate the

probability of waking in the sensor nodes which is

discussed further below.

3 DYNAMIC K-COVERAGE

3.1 Problem Definition and

Assumptions

A set of N sensors },...,,{

21 n

ssss

in a two

dimensional area A are distributed using Random

Uniform Distribution, Grid Distribution or Poisson

distribution. All sensors have the same sensing range

r. Sensor nodes have periodic sleep/awake cycles in

which a sensor node turns its sensing device on or

off. Duty cycle of the transmission device is

controlled by the MAC protocol.

Duty cycle of sensing device in turn follows the

scheme by the wakeup probability in each cycle

which our scheme assigns to each node.

Our assumption about number of deployed

sensors, probability of parallel occurrence of events

in the network and availability of location

information are similar to (Yahyavi et al., 2008).

Similar to (Yahyavi et al., 2008), we provide 1-

coverage over the entire surveillance zone. If an

intruder in the surveillance zone is detected the

wakeup probability is adjusted to provide asymptotic

k-coverage in the effective area of the intruder (a

circle around the intruder with radius , nodes in this

area are able to detect the intruder if they are active).

We use boundary conditions presented in (Kumar et

al., 2006) to determine the wakeup probability

required for different levels of coverage.

Consider that a function

)(np

is slowly growing

if it is monotonically increasing and

))(log(log(npO

and goes to infinity as

∞→n

. Let

)log(

)(

rnp

(1)

For the Random Uniform Distribution and

Poisson Distribution, for some

)(np

)log(log

))log(log()(

)(

npknp

≥

(2)

Then all the points are almost always k-covered

as n approaches infinity (Kumar et al., 2006). Where

n is the number of sensors deployed, p is the

probability of being active in each cycle, r is the

sensing radius of each sensor, and k is the level of

coverage. For Grid Distribution for some

)(np

)log(

))log(log())log(1)((

1)(

npknppnp

+≥

(3)

Then all the points are almost always k-covered

as n approaches infinity. (Kumar et al., 2006)

WINSYS 2008 - International Conference on Wireless Information Networks and Systems

Similar to (Kumar et al., 2006), since we

assumed that the number of deployed sensors is

sufficiently large, boundary conditions (2) and (3)

hold. These boundary conditions are used to

calculate the required wakeup probability of nodes

(for a fixed number of deployed nodes) to provide a

certain level of coverage. Goal is to find the

minimum probability that satisfies above boundary

condition for the required k. Minimizing the

satisfying probability lowers the number of active

sensors in each cycle and results in higher energy

savings (while providing k-coverage).

3.2 The Basic Model

In order to be able to detect any events in the

surveillance zone we need to have at least one sensor

cover every point in the surveillance zone. To

calculate the required wakeup probability to achieve

1-coverage from conditions (2) and (3) each node

should know the distribution type and the number of

deployed sensors. These parameters can be flooded

into the network after it has been deployed. The

required k should also be flooded in the network

along with these parameters. In case these

parameters change (the required k or n) they should

be re-flooded into the network.

All nodes primarily set their wakeup probability

to 1-coverage level. As an intruder enters the area

the only active sensor in that area issues a broadcast

message to alert neighbor nodes about the intruder.

Since MAC layer does not follow our sleep schedule

the detecting node may not be able to send the

ALERT message immediately and will have to wait

till the active period of MAC layer begins. All

neighbor nodes that hear this broadcast message

increase their wakeup probability to k-coverage

level. Therefore the number of active sensors in the

next cycle is increased to approximately k nodes.

In case an active node with wakeup probability

level k-coverage doesn’t detect an intruder it reduces

its wakeup probability for the next cycle to 1-

coverage level.

3.3 Misplaced K-Coverage Problem

Since only nodes in the communication range of the

first node that has detected the intruder can hear its

broadcast message and set their wakeup probability

to k-coverage level, some nodes in the effective area

of the intruder may not hear the ALERT message.

Also the intruder might be moving and the detecting

node may not be able to send the ALERT message

until the end of its MAC layer sleep period.

Therefore all nodes in the effective area of the

intruder may not hear the ALERT message. We call

this the misplaced k-coverage problem (Figure 1.a).

To address this problem, we present three

solutions; each one is more suitable for a different

situation and application.

3.3.1 Covered Effective Area Estimation

One of the methods to solve the misplaced k-

coverage problem is to choose a higher wakeup

probability so that the number of active nodes in the

effective area of the intruder is increased. This

solves the misplaced k-coverage problem but

increases the energy consumption in comparison to

the basic model. The number of sensor nodes that

are in the effective area of the intruder and in

communication range of the detecting node is related

to the movement speed of the intruder and the

density of nodes in the area. The faster the intruder

moves the less the number of nodes in the effective

area that can hear the ALERT message.

If the sensor node is able to determine the

location of intruder, the actual number of sensors

that can hear the ALERT message can be calculated:

=−=

∫

−

dxxrs

))

1(sin2sin()

1(sin2

1212

r −−−

−−

(4)

Where d is the distance from intruder to the

sensor node at the time of sending the ALERT

message.

Where d is the distance from intruder to the sensor

node at the time of sending the ALERT message.

Figure 1: (a) A randomly distributed wireless sensor

network and misplaced K-Coverage problem, (b) the

Effective Covered Area.

Therefore the number of sensors in the range of

ALERT message is:

=−

eragek )covPr(

(5)

(a)

(b)

r −

xry −±=

INTRUSION K-COVERAGE IN WIRELESS SENSOR NETWORKS

Where is the area that is covered by ALERT

message and

is the density of nodes

)(

. In our

simulations we assumed that the sensor node does

not have location and in a pessimistic guess

considered that only 75% of the effective area is

covered by the ALERT message.

3.3.2 Delayed Reduction

As mentioned in our basic model we reduce the

wakeup probability level of a sensor that has heard

ALERT message to 1-coverage level in case it does

not detect the intruder in the following cycle.

In the delayed reduction model each node

calculates and store k wakeup levels

},...,2,1{ k

. In

case the sensor node does not detect an intruder in

the cycle after receiving an alert message it reduces

its wakeup probability by only one level. Since the

node doesn’t decrease its level to 1-coverage

immediately we call this approach the delayed

reduction method.

This method is most useful when several

intrusions with the same movement pattern occur.

For example in border monitoring usually several

consecutive intrusions occur in the same area. In this

model after an intrusion occurs the network in the

intrusion’s area remains alert for the possible

consecutive intrusions.

3.3.3 Diffusion Model

In the diffusion model all active nodes that have

heard an ALERT message will rebroadcast the

ALERT message. In the Diffusion Model the

ALERT message also contains an alert level. Nodes

that hear this message set their wakeup probability

level to the level determined by the message.

In case a node detects the intruder it broadcasts a

message with alert level k otherwise it reduces the

alert level received by one level and rebroadcasts the

alert message. Similar to Delayed Reduction model

nodes decrease their wakeup probability level by one

level in each cycle.

In case a node receives several alert messages it

chooses the maximum alert level received as its alert

level. If a node receives an alert level lower than its

current wakeup probability level it will not

rebroadcast the ALERT message since all its

neighbor nodes already have equal or higher wakeup

probability level. This situation can happen in case

there is more than one intruder in the sensing area

and it has already caused higher wakeup probability

level in that area (Figure 2).



DiffusionModel

Dela

edReductionModel

k‐2

k‐1

k

k‐2

k‐1

k

Figure 2: Wakeup probability levels in Diffusion Model

and Delayed Reduction Model.

This approach is more suitable for situations

where the intruder’s movement is unpredictable and

high coverage on the intruder is required. This

approach provides higher coverage and reliability in

exchange for higher messaging and computational

overhead. The probability level in the intruder’s

effective area is almost always at least k.

Several methods can be used to reduce the

messaging overhead of this approach. For example if

the number of ALERT messages that a node hears is

more than a certain threshold it does not broadcast

an ALARM message. In case location information is

available ALERT messages from nodes closer than a

certain distance will not be rebroadcasted.

3.4 MAC Support

Several MAC protocol with energy saving features

have been proposed. Sensor nodes have different

energy consumptions in off, listening, receiving and

transmission modes (Chen et al., 2002). Putting the

transceiver to sleep also reduces the energy loss due

to overhearing avoidance.

Our approach requires support from the MAC

layer to make sure that neighbor nodes can hear each

other’s ALERT messages. IEEE 802.11 always

keeps the radio transceiver active which allows the

sensor node to send an ALERT message as soon as

an intruder is detected. On the other hand IEEE

802.11 has very high energy consumption. MAC

protocols that have Sleep/Active periods should

provide synchronous wakeup of neighbor nodes so

that at the end of sleep period nodes are able to send

the ALERT messages. S-MAC (Ye et al., 2004) and

T-MAC (Dam and Langendoen , 2003) support such

synchronous wakeups. We assume that reader is

WINSYS 2008 - International Conference on Wireless Information Networks and Systems

familiar with S-MAC (For further information

please refer to (Ye et al., 2004)).

Since after the initial synchronization period in

S-MAC the SYNC period is rarely used (to

resynchronize the schedules) we use this period for

our broadcast messages. In case there is a SYNC

packet waiting to be sent we can piggy back our

ALERT on the SYNC message. Otherwise an

independent ALERT message is created and sent

(RTS/CTS period can be similarly used).

In case the MAC protocol of our choice doesn’t

support synchronized wakeups of neighbor nodes, in

order to ensure that all neighbor nodes hear the

broadcast message, the node has to wake up at the

wakeup time of each one of its neighbors and

rebroadcast the ALERT message which increases

the energy consumption.

4 ENERGY CONSUMPTION AND

COVERAGE ANALYSIS

Dynamic reconfiguration of wakeup probability in

the area of an intrusion can provide significant

energy savings. Since the whole surveillance zone is

not k-covered number of active sensors in the

network is much less therefore overall energy

consumption of the network is substantially

decreased. Information required to calculate the

wakeup probability levels should only be flooded

once in the network. Probability levels are also

calculated and stored once at the beginning of

network’s deployment.

In the Effective Covered Area Estimation and

Delayed Reduction model the only messaging

overhead for dynamic reconfiguration is the ALERT

message sent by the first detecting node. Since the

ALERT message is a very small packet and may be

piggybacked this overhead is negligible. On the

other hand in Diffusion Model each node

rebroadcasts the ALERT message with a lower alert

level which poses higher messaging overhead.

Since our approach is probabilistic it doesn’t

need any location or probing information unlike

(Wang et al., 2003), (Tian and Georganas, 2002), (Ye

et al., 2002) to provide the requested coverage. Also

in case better and tighter boundary conditions for

wakeup probability to provide k-coverage are found

they can be easily replaced with current ones.

5 SIMULATION RESULTS

We evaluated different approaches presented in this

paper by simulation. Sensor network is deployed in a

mm 150150

area. Sensing and communication range

of nodes is 4m. Higher communication range than

sensing range helps the ALERT message to cover a

larger area. Static k-coverage calculates the wakeup

probability needed to provide k-coverage and

assigns this probability to all of the deployed nodes.

This method is compared to our dynamic k-coverage

approach and different solutions to solve the

misplaced k-coverage problem are compared. Each

simulation is run ten times and the requested

coverage in all simulations is 8-coverage (some

simulation results are not included due to space

limitations).

Figure 3: Average Coverage over an intruder for different

speeds.

Figure 3 shows how average coverage provided

by each method changes as the movement speed of

an intruder increases. As expected Static k-coverage

has the highest stability at the cost of higher energy

consumption. Diffusion Model and Covered Area

Estimation both provide high coverage for low

movement speeds but as the speed increases

Covered Area Estimation’s average coverage shows

a sharp decrease. This is because actual covered area

by ALERT message becomes smaller than our

guess. Diffusion Model has a more stable behaviour

which comes at the cost of higher messaging

overhead. The effect of misplaced k-coverage

problem on the coverage provided is clear.

Figure 4 compares the average coverage

provided over an intruder by different approaches. It

also shows that average coverage for these methods

doesn’t change as the number of nodes increases.

This means increasing the number of deployed

nodes will directly reduce the wakeup probability

and increase network longevity.

INTRUSION K-COVERAGE IN WIRELESS SENSOR NETWORKS

Figure 4: Average coverage over an intruder for different

number of nodes.

Figure 5 shows the average number of active

nodes in each cycle. As shown, to provide the

requested coverage always a fixed number of nodes

are required which means by increasing the number

of deployed nodes wakeup probability is reduced.

The main reason to use dynamic k-coverage is its

power efficiency. The number of active nodes in

each cycle is a very good measure of energy

consumption of each method. All dynamic

approaches wake less than a third of the static

approach.

Figure 5: Average number of active for different number

of nodes.

6 CONCLUSIONS

In this paper we presented a dynamic approach to

provide k-coverage over the area of an intrusion.

This approach provides 1-coverage over the

surveillance zone and k-coverage over the area of an

intrusion. Several solutions for misplaced k-

coverage problem which rises due to the dynamic

nature of approach are discussed. Each solution is

more suitable for a different kind of application. Our

simulations show dramatic improvement in energy

consumption of the network which results in higher

network lifetime. Our approach is completely

compatible with current popular MAC protocols in

WSNs. In this approach nodes do not need any

location information and due to its probabilistic

nature, minimal communication to provide k-

coverage is needed.

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