Coordination Based Motion Control in MobileWireless Sensor Network

Tathagata DasAlumnus Software Ltd

Salt Lake city

Kolkata, India

Email: dastathagata@gmail.com

Sarbani RoyDepartment of Computer Science and Engineering

Jadavpur University

Kolkata, India

Email: sarbani.roy@cse.jdvu.ac.in

Abstract—In the present scenario, there are substantialamount of application where mobile wireless sensor network(MWSN) would be better choice over the static wireless sensornetwork. The very nature of the MWSN environment requiressensor nodes to interact opportunistically to address a commongoal. Coverage is one of those areas where MWSN providesbetter solution. Coverage in static WSN depends on the initialdeployment strategy. Most of the recent works in MWSNpropose re-deployment strategy with the help of mobile nodes.In MWSN, the mobility of sensor nodes can be utilized toenhance the coverage of the network. One of the fundamentalproblems of MWSN is how to coordinate these mobile sensors insuch a way that they can move together to accomplish the giventask. Here, we study a generalized case of this problem. In thispaper, we present a coordination based motion control (CBMC)scheme where all mobile nodes form a cooperative group suchthat they cover an area by exchanging some key informationbetween themselves. The proposed scheme has two objectives:(i) each sensor node travels a minimum distance; (ii) minimumoverlap in the coverage path of sensor nodes. NS-3 simulationshows the effectiveness of CBMC scheme. The proposed schemeis compared with random way-point mobility model (RWP) andthree other variants of RWP.

Index Terms—Mobile Wireless Sensor Network (MWSN), Cov-erage problem, Coalition, Motion, NS-3

I. INTRODUCTION

Since mid-1990s Wireless Sensor Network (WSN) has

attracted tremendous interest of many researchers due to

its wide range of potential applications such as battlefield

surveillance, environment monitoring and biological detection.

WSN consists of thousands of tiny devices (motes or nodes)

equipped with sensors which can communicate with each other

using wireless medium. These devices are usually deployed,

either randomly or according to some predefined distributions,

in a large geographical area of interest where human move-

ments are restricted. Nodes are capable of sensing raw data

from the environment, processing those collected data and

sending them to the data sinks. A sensor node by itself has

severe resource constraints, such as low battery power, limited

signal processing, limited computation and communication

capabilities, and a small amount of memory; hence it can

sense only a limited portion of the environment [1]. However,

when a group of sensor nodes collaborate with each other,

they can accomplish a much bigger task efficiently. One of the

primary advantages of deploying a wireless sensor network is

its low deployment cost and freedom from requiring a messy

wired communication backbone, which is often infeasible or

economically inconvenient.

Mobile WSN (MWSN) is a particular class of WSN where

each node or some nodes have mobile capacity. Recently

researchers point out a substantial amount of applications

where MWSN would be better choice over static wireless

sensor network to collect raw data. For example, in health

monitoring system sensors are attached to patient body and

patients are allowed to move from one position to another.

In many situations, an optimal deployment is unknown until

the sensor nodes start collecting and processing data [2]. For

deployments in remote or wide areas, rearranging node posi-

tions is generally infeasible. However, when nodes are mobile,

redeployment is possible. In fact, using redeployment coverage

can be improved. In networks that are sparse or disjoint, or

when stationary nodes die, mobile nodes can maneuver to

connect the lost or weak communication pathways. This is

not possible with static WSNs, in which the data from dead

or disconnected nodes would simply be lost. Similarly, when

network sinks are stationary, nodes closer to the base station

will die sooner, because they must forward more data messages

than those nodes further away. By using mobile base stations,

this problem is eliminated, and the lifetime of the network is

extended [3]. In military surveillance where intruder detection

is one of the primary job, MWSN provides a better solution

than static WSN. In military surveillance, sensors usually

form coalition, based on some rule to detect intruder or even

trapping an intruder. Forming coalition dynamically is not

possible until and unless sensors are mobile. Mobility also

enables greater channel capacity and maintains data integrity

by creating multiple communication pathways, and reducing

the number of hops that messages must travel before reaching

their destination.

There are two types of applications found in MWSN as far

as the motion of sensor is concerned. In some applications,

motion can be controlled like military surveillance, vehicle

tracking etc. In military operations sensor may be deployed

on the body of soldiers or they may carry sensors for intruder

detection. Sensors can also be mounted on vehicles to dynam-

ically patrolling and monitoring the region of interest. In those

2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies

978-1-4799-2102-7/14 $31.00 © 2014 IEEE

DOI 10.1109/ICESC.2014.45

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cases motion of sensors is controlled by some logic. On the

contrary, in other application scenarios such as atmosphere and

under-water environment monitoring, airborne or under-water

sensors may move with the surrounding air or water currents

i.e. sensors move in uncontrollable fashion. In this paper, we

focus only on those applications where motion of sensors can

be controlled.

In WSN particularly in MWSN, coverage of total field is

a very important goal to achieve. The coverage of a sensor

network means how efficiently a region of interest is monitored

by deployed sensors and how effectively a sensor network can

detect intruders [4]. In static WSN, nodes remain stationary

after their initial deployment. Hence coverage in static WSN

is determined by the initial deployment. Once the deployment

and sensor characteristics are determined, coverage of that area

can be computed and it remains unchanged until and unless

some drastic changes in the network happens, which is again

very unlikely. However coverage in MWSN not only depends

on the initial deployment but also on the movement of the

sensors. Most of the recent works on coverage using MWSN

focus on the re-positioning of the sensors to proper positions

in order to improve coverage. So their mobility behaviour is

used to obtain a new stationary configuration that enhances

coverage after the sensors move to their proper positions.

In this paper, we examine coverage of MWSN from a

different perspective. Instead of finding a new stationary

configuration where coverage will be improved, we propose

an algorithm that uses the continuous movement of sensors

such a way that the area will be fully covered after a fixed

time. This coverage cannot be achieved if sensors stop moving.

Some enhancement of this algorithm can help us in intruder

detection and intruder trapping.

Rest of the paper is organized as follows. Section 2 de-

scribes related work whereas section 3 introduces the problem

of coverage in MWSN. Section 4 proposes the mathematical

model of the solution and the algorithm. Section 5 shows

the result and benefit over other existing algorithms. Finally,

section 6 concludes the paper and defines the scope of further

improvements.

II. RELATED WORK

In this section, we review work on area coverage in MWSN.

Most of the research work [5], [6], [7], [8], [9] on coverage in

mobile sensor network focuses on algorithms to re-position

sensors in desired positions in order to enhance network

coverage. More specifically, these proposed algorithms strive

to spread the sensors to desired locations to improve coverage.

The main differences among these works are how exactly

the desired positions of sensors are computed. Although the

algorithms can adapt to change environments and recom-

puted the sensor locations accordingly, sensor mobility is

exploited essentially to obtain a new stationary configuration

that improves coverage after the sensors move to their desired

locations. In another work [10], researchers assume that the

sensors are initially uniformly deployed and then they can

move independently in random directions. Based on this

model, they characterize the fraction of area covered at a

given time instant, the fraction of area ever covered during

a time interval, as well as the time duration that a location

is covered and not covered. The results suggest that sensor

mobility can be exploited to effectively reduce the detection

time of a stationary intruder when the number of sensors is

limited. It further presents a lower bound on the distribution of

the detection time of a randomly located intruder, and shows

that it can be minimized if sensors move in straight lines.

There are lots of algorithms to calculate area coverage of

mobile wireless sensor networks [11], [12], [13], [14], [15],

[16]. Many of these proposed algorithms strive to spread the

sensors to desired positions in order to obtain a stationary

configuration such that the coverage is optimized. The main

difference is how the desired sensor positions are computed.

In another work researchers study the coverage of a mobile

sensor network from a very different perspective. In this

paper, instead of trying to achieve an improved stationary

network configuration as an end result of sensor movement,

we concentrate on continuous movement of sensors to achieve

dynamic coverage.

III. COVERAGE PROBLEM IN MWSN

In this section, we will describe the underlying assumptions

and give the formulation of the coverage problem in MWSN.

A. Preliminaries

Let us consider a set of n sensors, S = {S1, S2, ..., Sn}forming a WSN that are randomly distributed in a contin-

uous region A. We assume that A is a 2D space. Each

wireless sensor node has an omni-directional antenna, so that

a transmission from a node can be received by all nodes

within its range. Each sensor is assumed to have same sensing

range (denoted by Rs) and communication range (denoted by

Rc). Although theoretically sensing gradient of a node can

be infinite but practically it gradually decreases as distance

increases. Suppose sensor node Si is located at a fixed location

and a point P ∈ A is sensed by the sensor. Then sensitivity ς of

that sensor is modeled as ς(Si, P ) ∝ 1d(Si,P ) where d(Si, P )

is the Euclidean distance between the sensor Si and the point

P . Binary sensing model is considered here. In this model,

the coverage of a point P by sensor Si, denoted by ϕ(Si, P ),can be described as the following equation.

ϕ(Si, P ) =

{1 covered, if d(Si, P ) ≤ Rs

0 otherwise, not covered(1)

Any two sensors Si ∈ S and Sj ∈ S can communicate

with each other if d(Si, Sj) ≤ Rc. We assume that the

communication range is always larger than twice of sensing

range (Rc ≥ 2Rs) [17]. This condition is both necessary

and sufficient to ensure that coverage implies connectivity.

Thus, connectivity between a pair of sensors does not ensure

overlapped coverage space. Further, we assume that each

sensor is aware about the existence and boundary location of

the given area of interest A.

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B. Problem formulation

The most studied coverage problem in static WSN is the

area coverage problem, where the main objective of the sensor

network is to cover a given region. Let us consider n sensors

are required to fully cover a continuous region A. Topology of

S i.e., Γ(S) is a graph with S as the set of vertices and there

exist an edge between any two sensor nodes Si, Sj ∈ S if they

can directly communicate with each other. Γ(S) also gives the

full coverage (at least 1-coverage of every point) of the given

area. Thus, in static WSN, any point P ∈ A is sensed by the

topology Γ(S) at any point of time.

On the other hand, coverage of a MWSN is resulting from

continuous movement of sensors. Initially all locations in

A are uncovered but as sensor nodes move around, these

uncovered locations are likely to be covered at a later time. A

given area A is covered as time continues with less number

of mobile sensors than static sensors. The area coverage

using MWSN is relevant for applications that do not require

simultaneous coverage of the entire area of interest at specific

time instants, but prefer to cover the given region within some

time interval. In the literature there are various discussions

about the coverage problem of WSN. In this paper, we employ

some results to formulate the coverage problem in MWSN.

Let us assume k sensor nodes (k < n), forming a MWSN

that are moving around and cover a given area A in [t0, te]where, t0 is the start time and te is the end time. As kincreases, time requires to cover the given region A decreases

and vice-versa. During the time period [t0, te] the given area

is thus partitioned into three regions:

1 Currently covered region- the region Ac(tc), which is

under the coverage of at least one sensor at current time

instant tc.

2 Region covered in recent past - the region Ap(t0, tc),which is covered in the recent past (start time to previous

time instant) by at least one sensor.

3 Uncovered region - the region Au, which is not yet

covered by any sensor.

A point is said to be detected if it lies and sensed within

the covered region Ac(tc) ∪ Ap(t0, tc) i.e., currently covered

or covered in recent past.

IV. COORDINATION BASE MOTION CONTROL

In this paper, we propose a self-deployment algorithm,

consists of mobile nodes, where total area is not always

covered rather it is covered in a fixed time period. This time

period depends on the number of sensors deployed in that

area. Initially sensor nodes are deployed randomly in a fixed

area. This area is divided into number of points, that are either

covered or uncovered, according to size of sensing radius of

sensor. If a point lies inside sensing radius of a sensor then

that point is considered to be a covered point. Covering all

these points implies that the total area is covered.

In this solution, all nodes are mobile and they are capable

of moving in eight directions; North, South, East, West,

North-East, North-West, South-East, South-West. Each node

maintains a list of points which are either covered by itself

or by other nodes. Each node periodically shares its own list

of covered points with other nodes. This is accomplished by

sending and receiving packets that consists of node id, number

of covered points and list of covered points as shown below.

id cp lcp

TABLE ISEND/RECEIVE PACKET FORMAT

1) id: contains the unique identification number of the

source node

2) cp: Number of points that are covered till now, either

by itself or by other nodes

3) lcp: List of covered points

Packet sent by one node is received by all other nodes who

are in the communication range of that node. Thus at any point

of time each node knows which points are still uncovered.

Usually node doesn’t change its direction unless it reaches

either to the boundary of the area or to a point in its path that is

already covered. At that time it calculates the direction from its

current position to its nearest uncovered point and accordingly

move towards that new direction. In this way node keeps on

moving until all points are covered. So we can say that each

node follows a path that depends on its initial direction and

movement of other nodes. But it does not depend on the initial

deployment strategy.

Here we use the concept of coalition to some extent. If

each node individually cover the area then it would take huge

amount of time to complete. On the contrary less time is

required if every node move jointly like a group. In that case,

area is considered to be covered if all points are covered by

that group. This is similar to the advantage of cooperative

game theory over non-cooperative game theory. In cooperative

game theory players are allowed to form coalition to share

decision, information and payoffs. In our case, only one group

or coalition exists which consists of all nodes and this coalition

follows rules of cooperative game theory.

Similar to cooperative game theory, this solution consists

of two elements: (a) a set of players (here set of nodes),

and (b) a characteristic function specifying the value created

by different subsets of the players in the game. Formally,

let S = S1, S2, ..., Sn be the (finite) set of players and

the characteristic function is a function, denoted by v, that

associates a number, denoted v(S). The number v(S) is

interpreted as the value created when the members of S come

together and interact. In sum, a cooperative game is a pair

(S, v), where S is a finite set and v is a function mapping

subsets of S to numbers. Here we have only one coalition and

value of characteristic function is the time required to complete

coverage of the area. In this case, value of characteristic

function is inversely proportional to the time.

Any coalition in cooperative game theory must support

individual rationality and efficiency. Individual rationality says

that a division of the overall value (i.e. an allocation) must

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give each player as much value as that player receives without

interacting with the other players. Efficiency says that all the

value that can be created, i.e. the quantity v(S), is in fact

created. Suppose this coalition is simply a division of the

overall value created, and the quantity xi denotes the value

received by player i.Individual rationality: ∀i xi ≥ v(i)Efficiency:

∑ni=1 xi = v(S)

In the result section, it is shown that time taken to cover

an area is inversely proportional to the number of nodes. It

is evident that time taken by a single node is always higher

than time taken by all nodes. Hence nodes are benefited if they

move jointly instead of moving independently. So our solution

follows individual rationality.

v(x1) ≤ v(S)In our solution, definition of efficiency would be slightly

changed.⋃ni=1 xi = v(S)

This is because here we deal with time taken by each node and

these nodes move simultaneously. Here v(S) is the maximum

time taken by a node among all nodes. That means this

algorithm satisfies both individual rationality and efficiency.

A. Algorithm

Each sensor has uniform velocity v, fixed sensing radius

rs and communication radius rc where rc ≥ 2rs. Sensors

are deployed in a fixed area and it is of polygon shape.

Each sensor knows its own current position and maintains

co-ordinates of vertices of the polygon so that it is aware

of the boundary of that polygon. Each sensor can move

in eight directions; East, West, North, South, North-East,

North-West, South-East and South-West.

Each node contains some information; some of them are

constant for all nodes and others are dynamic.

Name Descriptionid Unique node idrs Sensing radiusrc Communication radiust Time interval of sending broadcast packetv Velocitybndpos Co-ordinates of vertices of the polygoncurpos Current position; x,y co-ordinates of the nodetp Total number of pointscp Number of covered pointslistofcps List of covered pointsdir Current direction

TABLE IINODE STRUCTURE

function INIT

/* Initial random deployment */

curpos← random value

bndpos← co-ordinates of all vertices of that polygon

/* Initialize total number of points in that given area */

tp← 2A√27R2

s

[18]

/* Initialize number of covered points as 0 */

cp← 0/* Initialize list of covered point as empty */

listofcps← ν/* Randomly select direction of movement */

dir ← random direction

Move(dir)

end function

function MOVE(dir)

if cp == tp thenShow the time

Stop /* Algorithm terminate */

end if/* Before moving to next position, update its list of

covered points

* First based on its current position */

listofcps← listofcps⋃curpos

cp← cp+ 1/* Second by exchange information with neighbour

nodes.

* Send broadcast packet containing its list of covered

point */

sendpkt(id, listofcps, cp)

/* Receive broadcast packet from neighbour nodes */

if recvpkt(neighbour.id, neighbour.listofcps, neighbour.cp)

thenlistofcps← listofcps

⋃neighbour.listofcps

cp← cp+ neighbour.cpend if/* Calculate next position of movement based on its

current position and direction of movement */

nextpos← curpos+ dir/* Check if next position is outside boundary or it is

already covered */

if nextpos ∈ bndpts or nextpos ∈ listofcps then/* Decide next direction */

dir ← DecideNextDir(cuspos, listofcps, dir)

/* Recalculate next position */

nextpos← curpos+ dirend if/* Move to next position */

cuspos← nextposMove(dir)

end function

function DECIDENEXTDIR(curpos, listofcps, dir)

for pt = 0→ all points doif pt /∈ listofcps then /* Select uncovered point */

dist← distance between pt and curposif dist < mindist then

mindist← distnewpos← pt

end ifend if

end for

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newdir ← (curpos→ newpos)

return newdirend function

V. EVALUATION

Building a MWSNs test bed is very costly. Running real

experiments on a test bed is costly and difficulty. Besides,

repeatability is largely compromised since many factors affect

the experimental results at the same time. It is hard to isolate a

single aspect. Moreover, running real experiments are always

time consuming. Therefore, MWSNs simulation is important

for MWSNs development [19]. CBMC is tested in a simulation

environment in order to demonstrate its functioning and its

effectiveness. The simulation environment is generated by

using NS-3 [20].

Network simulator NS-3 is free, open source and relies

on C++ for the implementation of the simulation models. It

provides advanced simulation environments for testing and

debugging any kind of networking protocols, including mobile

wireless sensor network. Various standard mobility models are

already available in NS-3. In NS-3, we implement CBMC

using C++ and tested it in different topology and with various

number of nodes. This test scripts are also written in C++.

CBMC is compared with different versions of Random

Way Point (RWP) model. In basic RWP, velocity of nodes

are random. We modify RWP (RWPCV) such that velocity is

constant. In RWP and RWPCV, nodes can traverse same point

more than one whereas there are still some uncovered points

present. We modify these two algorithms where any point is

not traversed multiple times. Thus we compare CBMC with

four different algorithms.

• RWP: Random Way Point model without any modifica-

tion.

• RWPCV: Random Way Point model where each node has

constant velocity.

• MRWP: RWP with a modification that any point is not

traversed multiple times.

• MRWPCV: RWPCV with a modification that any point

is not traversed multiple times.

A. Results

We consider an environment which consists of maximum

100 nodes. In order to compare the functioning of the proposed

algorithm with other versions of RWP (as mentioned above),

number of nodes in the network has been varied from 10 to

100 in an increment of 10. We also consider two areas of size

1000 x 1000 and 2000 x 2000.

Fig. 1. Time required to cover area of size 1000 x 1000

Fig. 2. Time required to cover area of size 2000 x 2000

Fig. 3. Percentage of covered area of size 1000 x 1000

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Fig. 4. Percentage of covered area of size 2000 x 2000

In first two figures, size of the area remain same and we vary

number of nodes. There we plot the time taken to complete

the area with given number of nodes. As we can observe that

with increase of number of nodes; time required to cover the

area decreases. In last two figures, size of the area and time

remain constant. There we plot percentage of area covered in

that time. In first case (figure 3), we execute it for 15 seconds

whereas in second case (figure 4) we execute it for 95 seconds.

We also observe that percentage of area covered is inversely

proportional to the number of nodes deployed in that area.

First two figures show that with same number nodes, among

themselves CBMC takes minimum amount of time to cover the

area. It can also be noted that compare to RWP and RWPCV,

modified version of RWP and RWPCV perform better. This

is mainly because in CBMC as well as in modified version

of RWP and RWPCV, all nodes form a coalition where they

exchange information about which points are already covered

and which are not yet covered. Third and fourth figure also

prove this point that forming coalition provides better result

if coverage of the area is the primary aim of the application.

VI. CONCLUSION

In this paper we propose a motion control algorith, Coor-

dination Based Motion Control (CBMC). The algorithm has

been proposed for executing jobs in some typical situation like

intruder trapping, military surveillance etc. CBMC uses the

concept of cooperative game theory by forming a grand coali-

tion. This coalition improves coverage time with mobile nodes.

However, in the current implementation of the algorithm, we

did not use cooperative game theory in making decision after

exchanging information between nodes. Also nodes did not

share its next movement with others. So there is a probability

that nodes will collide with each other. Hence we can improve

the coverage time if we consider future movement of other

nodes and share that information too. Further improvements

of this algorithm must be made and it must be tested in actual

environments.

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