Content uploaded by Khaled Elleithy
Author content
All content in this area was uploaded by Khaled Elleithy on Jan 12, 2014
Content may be subject to copyright.
Content uploaded by Khaled Elleithy
Author content
All content in this area was uploaded by Khaled Elleithy
Content may be subject to copyright.
CATA-2013, March 4-6, 2013, Honolulu, Hawaii, USA
Efficient Search (RES) for One-Hop Destination over Wireless Sensor Networks
1
Abdul Razaqueand
2
KhaledElleithy
Wireless and Mobile communication (WMC) Laboratory
Department of Computer Science and Engineering
University of Bridgeport-06604, USA
1
arazaque,
2
elleithy {@bridgeport.edu}
Abstract: -The revolution of wireless sensors networks
(WSNs) has highly augmented the expectations of people
to get the work done efficiently, but there is little bit
impediment to deal with deployed nodes in WSNs. The
nature of used routing and medium access control (MAC)
protocols in WSNs is completely different from wireless
adhoc network protocols. Sensor nodes do not have
enough capability to synchronize with robust way, in
resulting causes of longer delay and waste of energy. In
this paper, we deploy efficientenergy consuming sensors
and to find one hop robust and efficient destination search
in WSNs.
We firstly deploy BT (Bluetooth enabled) sensors, which
offer passive and active sensing capability to save energy.
This work is a continuation of previous published work in
[2]. The BT node is supported with efficient
searchmethodss. The main objective of this contribution is
to control different types of objects from remote places
using cellular phone.
To validate our proposed methodology,simulation is done
with network simulator (ns2) to examine the behavior of
WSNs. Based on simulation results, we claim that our
approach saves 62% energy spent for finding best one-
hop destination as compared with existing techniques.
General Terms: Design, Experimentation, Performance,
Algorithms.
Key Words and Phrases:BT sensor, WSNs,
preservingenergy, one-hop efficient search.
1. INTRODUCTION
The research community have been observing swift
scientific and technological advancement since last four
decades. The technological progress in compactness of
microprocessors has made significant expansionin WSNs
[4]. WSN is a fast growing segment attracting people
around the world [6].
The goal of WSNsis to facilitate acommunication bridge
between the users and the environment using sensors
andcomputing devices. WSNs also combine several
professions through many application domains, e.g.,
health, education, security and social care. Many devices
are now embedded with computing power like home
appliances and portable devices (e.g., microwave ovens,
programmable washing machines, robotic hovering
machines, mobile phones and PDAs). These devices help
and guide us to and from our homes (e.g., fuel
consumption, GPS navigation and car suspension) [10].
Ambient Intelligence (AmI) involves compact power that
is adapted to achieve specific tasks. This prevalent
accessibility of resources builds the technological layer
for understanding of WSNs [3].
Information and communications technologies (ICT) have
highly been accepted as part of introducing new cost-
effective solutions to decrease the cost of energy in WSNs
[2]. For example, the use of WSN in home that is
equipped with AmI to support people in their homes.
These developments have led to the introduction of
energy efficient consuming sensors that are capable of
monitoring the constraints of humans as well as living
environment [5].
Furthermore, expansion in sensors, along with advances
of software applications, makes it possible today to
control any deviceanytime and everywhere but
consumption of energy is an immense problem. These
solutions not only improve the quality of education,
security and health of people in their own homes but also
provide afaster way of communication to interact with
devices all over the world [9]. Meanwhile, the exploitation
of mobile devices in WSNs provides more flexibility,
intelligence and adaptivity to interact with devices
dynamically in any environment [12].
Such technological advances make it possible to deploy
mobile phones not only as terminal, but also as remote
controller for several devices [2, 11]. In this paper, we
introduce a novel paradigm to facilitate controlling remotely
available servers and different devices using minimum
energy consumption.
2. PROPOSED ARCHITECTURE
WSNs support more complicated communications
between multiple agents without limitations of time.
These agents get input and output to facilitate users to
achieve potentially multifaceted tasks at high speed
[13].The current systems supply some level of adequate
service within specific boundaries. The proposed
architecture in this paper consists of specific type of BT
sensors and mobile phone. It is used to interact with
different types of servers and other devices. Initially,the
mobile device is deployed to control remote servers and
several types of devices through WSN.
The basic architecture of this paradigm is shown in figure
2 that only targets ubiquitous communication.The BT
node provides self-directed prototyping platform based on
microcontroller and Bluetooth radio. Bluetooth has been
introduced as exhibition platform for research to deploy in
distributed sensor networks, wireless communication and
ad-hoc networks. It is composed of microcontroller,
separate radio and ATmega 128. The radio of BT node
comprises of two radios: first is low power chipcon
CC1000 suited for ISM-band broadcast. It works same as
Berkeley MICA2 mote does. This supports BT node to
create multi-hop networks. Second radio is Zeevo
ZV4002 supported with Bluetooth module.
The BT node provides multiple interfaces to control many
devices at the same time but it consumes of lot of energy
during the sensing time. To resolve this problem we
implementrobust and efficient search at one-hop
destination method.We use different protocols and
standards in our architecture. Most of sensors do not
communicate with Zigbee/ IEEE 802.15.4 standard [2].
Wi-Fi and Bluetooth are also not compatible because both
utilize unlicensed 2.4 GHZ ISM bandwidth. So they are
using the same bandwidth which causes interference
between them. In addition, both are transmitting data at
binary phase shift keying (BPSK) and quadric phase shift
keying (QPSK). Our selection of BT node sensors is to
provide the compatibility with Zigbee, Bluetooth and Wi-
Fi. It supports different types of applications and having
multitasking support.
Figure 1 shows simple architecture that supports WSN
applications. BT node consists of several drivers that are
designed with fixed length of buffers. The buffers in BT
node are adjusted during compile time to fulfill severe
memory requirements. Available drivers include real time
clock, UART, memory, I2C, AD converter and power
modes.
In this paper we introduce a theoretical framework that
facilities compatibility of mobile phoneswith BT node
sensors over wireless sensor networks. We install Asus
WL-500GP that maintains IEEE 802.11 b/g/n standards
that is equipped with USB port to interlink with sensors.
We also use Zigbee USB adapter/ IEEE 802.15.4 to
provide the communication between sensors. Zigbee/
IEEE 802.15.4 also provide the capability to sensors to
maintain multi-hop communication. USB ports and
adapters provide promising platform to interlink the
mobile phones with sensors for establishment of
connectivity to control remote devices. We deploy a
highly featured wireless sensor network shown in figure
2.
BT NODE rev3
SENSOR
UART
DRIVERS
12CGPIORTC
AD
CONVERTER
COMMUNICATION TECHNOLOGY
IEEE 802.15.4 ZIgBee Bluetooth
APPLICATION
JOB-NJOB-VJOB-IVJOB-IIIJOB-IIJOB-I
Figure 1: Architecture for Wireless sensor application
The beauty of our WSN is distribution into different
regions. Each region has one boundary node that
coordinates with boundary node of other regions. The
coordination process is also validated with lemma and
several definitions discussed in section 3. Participating
sensors go automatically into active and passive modes
for saving the energy [2]. The working process approach
makes WSN to find faster and robust search.
N1
BOARDER
NODE
Region-1
N22
N20
N18
N27
N24
N14
N53
N52
N44
N37
N38
N23
N34
N40
N39
N43
N28
N30
N42
N41
N41
N15
N35
N17
N31
N26
N25
N36
N29
N13
N19
N16
N28
N21
N12
N11
N10
N9
N8
N7
N6
N5
N4
N3
N2
BOARDER
NODE
BOARDER
NODE
BOARDER
NODE
N50
N57
N46
N45
N54
N49
N58
N1N51
N56
N47
N55
N48 N60
N59
Mobile
Wi-Fi Access point
Authentication
Server
Cache server
Mobile supported content Server
Logical
connection
Logical connection
Internet
IEEE
802
.
15.
4
/
Zegbee
/
Bluetooth
IEEE802.11b/g/n
WLAN
N0
Region-2
Region-3
Region-n
Figure 2: Proposed architecture for WSN
CATA-2013, March 4-6, 2013, Honolulu, Hawaii, USA
3. Efficient Search for one-hop destination
This search is purely based on 1-hop shortest path
information and route discovery. The mechanism
separates global topology and local connectivity. Suppose
that direct graph D = (V, A) is consisting of the set of
sensor nodes V. The set of edges are called arcs that are A
V2. It helps to differentiate between 1-hop destination
and more than 1-hop destination nodes. The digraph
distance between nodes is simply the number of shortest
path between them [7]. We assign name to each sensor
node in V. The local route discovery method is based on
relay scheme that works as discussed below.
For any destination node in ‗V‘ is specified by name v,
the scheme targets the 1-hop destination nodes u on basis
of stored information in routing table regarding shortest
path 1-hop destination node. Each 1-hop destination node
provides the shortest path to its predecessor; finally
destination v is obtained with shortest path. We apply the
method in [8] for estimation of global technology of
sensors by dividing nodes into routable boundaries and
extracting adjacency associations between these
boundaries. The objective of creating each boundary is to
make the topology simpler, so that routing process works
efficiently within boundaries. For set of sensor nodes ‗V‘
and communication digraph ‗D‘, we assume D is
connected, since we just considered connected
components independently. We denote u : (u,v) A is
hop count of the shortest path between u,v in
communication digraph.
Definition 1. For a digraph D = (V, A), the set of 1-hop
destination nodes for vertex v that is explained as Γ−D(v)
= {u : (u,v) A}, and beyond of 1-hop destination nodes
are explained as Γ+D(v) = {u : (u, v) A}.We explain 1-
hop destination nodes of a vertex v as union with set of
1-hop destination nodes and the set of more than 1-hop
destination nodes, ΓD(v) = Γ+D(v) U Γ−D(v). It has out-
degree and in-degree deg+D (v) = |Γ+D (v)| and deg−D
(v) = |Γ−D (v)| respectively.
It is clear that degD (v) ≠ deg−D (v) + deg+D (v) and
parity do not essentially hold. We may again exclude
subscript if digraph DG = (V, A) is clear from context.
The weighted graphs also get association of assorted
length, cost and strength. We only focus on edge-
weighted graph that is opposite to node-weighted graphs.
We also need to restrict edge weights to 1 that capitulate
an un-weighted graph. Consider digraph DG = (V, A) and
its subset for regions R V, explain boundary B (v) of a
node. Therefore, v R and whose nearest region is v. It
can formally be written as:
B (v) = {u V | w R, τ (u, v) ≤ τ (u, v)}
Lemma1. For any node u B (v), the shortest path from
node u to v is completely included in B (v).
Proof: If lemma were incorrect, there would exist w ≠
B(v) on the shortest path from node u to destination node
v. Therefore τ(w,v) < τ(w,u). and such that τ(x,v) ≤
τ(x,w)+ τ(w,v) < τ(x,w) + τ(w,u) = τ(x,u). This statement
contradicts with hypothesis such as x B(u); thus lemma
must be correct. One inference of this lemma is
connection of boundary cells on spanning graph.
Boundary cells are dirichlet, connecting all points of
sensor field. Boundary has simple topology in all
dimensions that is stronger point of connectivity. The
simpler topology helps to make subsets of sensor fields,
when sensor filed experiences large holes. Thus, edges
u1, u2 є B (v).
Definition 2. Let PDG (x, y) denote set of paths from x
to y in direct graph (DG). Hence, SDG (x, y) is the
distance (S) between nodes x, y in DG, which shows
shortest path from node x to y. It can be computed as:
,
= min l
,
(1)
If SDG (x, y) = then SDG (x, y) = ∞, Therefore SDG(x,
Ѐ) between node x and subset of nodes Ѐ E that is
defined as:
, Ѐ = (2)
Thus, Ẋ, Ѐ E, it is distance between two subsets that can
be computed as:
min (, )& Ѐ(3)
add random infinitesimal if unique path is required.
Lemma 2. Let simple path P= (d, a1, a2,…,a
e-1,
t) that
connects two boundaries nodes d= a1 and t= a
e
with e
edges and path of length is l(P). The related boundary
path p* has maximum length in boundary dual graph
(BG*) such as l(P*) ≤ e * l(P*).
Proof: The path includes e-1 more than 1-hop destination
nodes and e edges that pass through at most e +1 in the
different boundaries of regions. The most of regions e-1
are intrusive regions, it means that original path does not
direct through related boundary nodes but shortest
boundary path does. The l(P) in original graph is sum of
edge weights that can be defined as:
l
P
= d
s, t
= w
ai, ai + 1
1
=0
(4)
d* (B
bou
(a
i
), B
bou
(a
i
+1) (5)
It is edge (e) between two nodes of boundaries on path P*
that is bounded as follows:
P*= [d* (B
bou
(a
i
), B
bou
(a
i
+1)≤ d (B
bou
(a
i
), a
i
) + W ( (a
i
),
(a
i
+1) + d ((a
i
+1) , B
bou
(a
i
+1))] (6)
From the set boundary of regions, we observe that d (a
i
, a
i
+1) ≤ d(a
i
, B
bou
(a
k
) for all k.
On basis of assumption, we say that s and t are also
boundary nodes that could be source and target nodes
defined as follows:
d(a
i
, B
bou
(a
i
) ≤ d (s, a
i
)d(a
i
, B
bou
(a
i
) ≤ d ( a
i,
t)= d(B
bou
(a
i
),
a
i
) : It yields:
l(P*) ≤ d* (s, t) = d* (s, B
bou
(a
1
)
[d
(
) ,
+ W (
,
+1)
2
=1
+ d(
+
1,
(
+1
)
+1
)] + d* (B
bou(ae-1)
,t) ≤ w(s,ai) +d(ai,B
bou(ai)
)
(7)
[d
(
) ,
2
=1
+ d(
+ 1,
(
+
1))] d
,
+1 ,
2
=1
+ d(
(
1),(
1, t)
(s, t) + d
,
,
2
=1
+ (
,,
) ] (8)
Simplifying the equation (8), we get as:
e. l
P
(9)
Bound is found to be tight because constructions exist.
For example, If any choice for m > є > 0, edge weights of
graph should be selected and given as follows:
d(a
i
, B
bou(ai)
) = m — є, w(a
i
, a
i
+1) =є (10)
and
w(s, a
i
) = w(a
e-1
,t)= m (11)
Since 2m + (e—2) є is the length of given path and 2m
+(e-2) є 2(e-1)*(e- є) is the length of boundary path.
Therefore, the worst case for ‗є‘ can be written as:
Є → 0, and ratio can be shown as follows:
(12)
If boundary nodes are available on shortest path, thus
maximum expansion is assured to be shorter than number
of edges on shortest path. We hereby prove that maximum
expansion is proportional to largest gap between
boundary nodes on path.
Lemma 3: For each region ‗R‘, the flooding message
(R
n,
f
m
) =1 can provide the shortest distance region to each
node in Sensor network N.
Proof: Here R
n
is number of regions and f
m
is flooding
message that can be sent from one region to other region.
Thus, each node v maintains the list of current shortest
path region (R
v
) with shortest distance (D
v
)in network.
Assume R
v =
and D
v =
∞. Therefore, on obtaining
flooding message in any region (R
n,
f
m
), we apply three
conditions as follows:
Condition-I:
iff
m
>D
v
[messageis discarded]
Condition-II:
iff
m
= D
v
, then we apply two cases:
ifR
n
R
v
[messageis discarded]
A. if R
n
R
v;
then
R
n
+ R
v
and [Broadcasted message] to all the neighbor
nodes.( L
n
, f
m
+1) [Broadcasted message]
Condition-III:
if f
m
<D
v; ;
thenR
v
= (R
n
) , D
v
= f
m
and( R
n
, f
m
+1)
[Broadcasted message] to all the neighbor nodes.
If regions initiate the process at same time, every message
travels at the same speed. Thus, f
m
messageof any region
is dropped when it begins to penetrate in the boundaries
of other cells. This causes of cutting down the transmitted
messages in flood. At the end of process, the list R
v
holds
set of regions which are at the shortest distance to node v.
At this point, each node knows its boundary of region in
sensor network N. Hence, the flooding message (R
n,
f
m
) =1
provides the shortest path to node.
4. SIMULATION AND ANALYSIS OF RESULT
Real wireless sensor environments use low power radios
and are known due to high asymmetrical communication
range and stochastic link attributes. The simulation results
could be different from realistic experimental results [1].
If network simulator makes only simple assumptions on
wireless radio propagation, exact simulation with features
of real wireless radios and diverse transmission powers
can be significant. The WSN is distributed into different
regions as illustrated in figure 2 to make the sensors more
convenient to collect information quicker.
We have already discussed about boundary node that is
playing role as anchor point (AP) or head node. We have
set one boundary node in each region. Boundary node
forwards the collected information of its region to next
region. In our case, it is not necessary that boundary node
may always coordinate with only boundary node of other
region but it can forward the gathered information at 1-
hop destination either boundary node or common node.
We have simulated several types of different scenarios by
increasing the number of sensors.These scenarios are real
test of WSN. We have deployed 35 to 140 sensors within
network area of 160m × 160m. Area is divided into 40m x
40m regions. Sensors are randomly located within each
region.
The sink in each scenario is located at (140, 60), but
bandwidth of node is 50 Kbps and maximum power
consumption for each sensor is set 160 mW, 12 mW and
0.5 mW for communication, sensing and idle modes
respectively but in our case, there is no idle mode.
Sensors either go to active or sleep mode. Each sensor is
capable of broadcasting the data at 10 power intensity
ranging from -20 dBm to 12 dBm. Total simulation time
is 140 minutes and there is no pause time during the
simulation but we have set 30 seconds for initialization of
phase at beginning of simulation. During this phase, only
sensors onto sink remain active and remaining sensors of
all regions go into power saving mode automatically. We
have chosen well known energy saving approaches. We
have specially checked the performance of existing
approaches at routing level. These existing approaches
include minimum energy relay routing (MERR),
minimum-transmission-energy (MTE), direct
Transmission (DT) and optimal routing (OR).The results
shown in this section are average of 10 simulation runs.
CATA-2013, March 4-6, 2013, Honolulu, Hawaii, USA
4.1 Efficiency of proposed WSN
To valid this environment of WSN for handling several
devices, we conduct several tests from different
perspectives. Having presented the mathematical models,
we now evaluate the efficiency of WSN. We present the
amount of energyused in the network for a number of
sensors as shownin Figures t to Figure 6
0
5 10 15 20 25
30
35
20 40 60
80
100
Number of sensors
Total Energy Preservation %
0
MERR
MTE
DTE
OR
RES
Figure 3: Preservation of energy in % with 35 sensors
0
10 20 30 40 50
60
70
20 40 60
80
100
Number of sensors
Total Energy Preservation %
0
MERR
MTE
DTE
OR
RES
Figure 4: Preservation of energy in % with70sensors
Our simulated network shows that our proposed paradigm
achieves almost 100% efficiency and saves 62% energy
using maximum 140 sensors. We establish 15 sessions
simultaneously in order to determine the actual behavior
of the network in highly congested environment. If we
have less number of sensors, it is hard to establish many
sessions at the same time.It is very noticeable direction of
this research that 140 sensors can provide path-
connectivity for 15 mobile devices to interact with
remotely placed devices at same time. In addition, one
mobile node can interact with multiple devices at same
time. Question is why to deploy more sensors in that
area? The answer is the availability of several servers and
devices at the different places.
0
15 30 45 60 75
90
105
20 40 60
80
100
Number of sensors
Total Energy Preservation %
0
MERR
MTE
DTE
OR
RES
Figure 5: Preservation of energy in % with105sensors
0
20 40 60 80 100
120
140
20 40 60
80
100
Number of sensors
Total Energy Preservation %
0
MERR
MTE
DTE
OR
RES
Figure
6: Preservation of energy in % with140sensors
More sensors are required to find path and provide the
connectivity for enough number of mobile devices
working concurrently. Figure 7 shows the trend of
network during the time of 140 minutes of simulation at
the same number of established sessions. During this
duration, the mobile devices get 99.2% coverage of
network whereas existing network affects the
performance of network as time increases of
communication. This is another weakness of
existingWSNs. Our proposed method produces stable
efficiency during all simulation time. It is proved that
duration of simulation either increases or decreases; do
not affect the efficiency in our case. The minimum
number of sensors required for covering area can be
calculated as follows:
N
min(s)
=
2
3
2
; WhereN
min(s)
is minimum number of sensors
to cover whole area to maintain connectivity and
coverage. ‗r‘ is sensing range of sensor. It should be
assumed that sensing range is smaller than dimensions of
monitoring area.
Nmin(s)
Nmax(s)
shows maximum number of
sensors. ‗R‘ is distance of total network. We prove this
with help of lemma4.
Lemma 4:
Nmin(s)
Nmax(s)
is upper bound on R and N
min(s)
is lower
bound on S
i
, where N
min(s)
=
2
3
2
.
Proof:Let upper bound be linear on R with maximum
number of sensors (total number of sensors) N
max(s)
whereas lower bound on S
i
is invariant with N
max(s)
. In
addition, these bounds are not considered tight as long as
they do not consider transmission radius ‗T
r
‘ of sensors.
However, we need better heuristic solution to follow these
bounds closely if irrespective of changes occur in the
parameters of network. Hence, the lifetime of the network
should be linearly with N
max(s)
, and Si to be constant with
N
max(s)
.
0
20 40 60 80 100
120
140
0.2 0.4 0.6
0.8
1
Simulation time in minutes
Coverage Efficiency of WSNs with different approaches
0
MERR
MTE
DTE
OR
RES
Figure 7. Coverage efficiency of network at different intervals
5. CONCLUSION
In this paper, we have introduce anefficient search
algorithmfor one-hop destination to save energy that
provides access to control remotely available servers and
several types of devices through mobile cell. This unique
type of research deploys BT sensors in wireless network
to control the devices. Furthermore, WSN is divided into
number of N-regions. Each region consists of one
boundary node that is static and is responsible to
communicate with other regionsfor saving energy. When
the sensor finishes its assigned task, it automatically goes
to sleep mode. To validate the proposed WSN, we
havesimulated the network using ns2.35-RC7. On the
basis of simulation results, we prove that our proposed
research saves maximum amount of energy as
comparedtoexisting WSNs of saving energy. In addition,
we have achieved the objective ofcontrolling the devices
from remote places by consuming minimum energy
resources. In the future, we are planningto implement this
simulation based network into testbed to control several
devices simultaneously.
REFERENCES
[1] J.Zhaoand R.Govindan, ―Understanding packet delivery
performance in dense wireless sensor networks‖,In
proceedings of ACM, (Los Angeles, CA, November 5-7,
2003.
[2]A. Razaqueand K.M.Elleithy,‖Automated energy
saving(AES) paradigm to support pedagogical activities
over Wireless sensor networks,‖ Ubiquitous Computing
and Ambient Intelligence (UCAmI), Springer, 3-5
December, 2012.
[3]M. Milradand D.Spikol, ―Anytime, Anywhere Learning
Supported by Smart Phones: Experiences and Results from
the MUSIS Project. Educational Technology & Society, 10
(4), 62-70.
[4]J.Chin ,V.Callaghan and Clarke.G,‖Soft-appliances: A vision
for user created networked appliances in digital homes.
Journal on Ambient Intelligence and Smart Environments
(JAISE), 1(1), 69–75, 2009.
[5. A.H.Nakashimaand J.C.Augusto, ―Handbook on Ambient
Intelligence and Smart Environments‖, Springer Verlag,
89-115.
[6]B. Chang, B. M. Lim, M. Ham, D.H. Lee, Y. Kang and S.J
Cha,‖ Supporting Multi-device for Ubiquitous Learning‖,
Lecture Notes in Computer Science. 4469, 457-467.
[7]C.A.B. Ioannis, A.Kinalisa andS.B.Nikoletseasa, ‖Fault
tolerant and efficient data propagation in wireless sensor
networks using local additional network information‖,
Elsevier International Journal of Parallel and distributed
computing 67 (2007), 456–473.
[8]J.Q. Fang, L.J. Gao, L. J.Guibas, V. de Silva and L.Zhang, ―
GLIDER: gradient landmark-based distributed routing for
sensor networks‖, In Proceedings of the 24th Annual Joint
Conference of the IEEE Computer and Communications
Societies (INFOCOM), Vol. 1 (2005), 339-350.
[9]J.Bohn, V. Coroama, M. Langheinrich,F.Mattern and M.Rohs,
―Social, Economic, and Ethical Implications of Ambient
Intelligence and Ubiquitous Computing‖,Vol: 10(5), 15
October. 2004.
[10] D.J Cook, C. J.Augusto and R.V.Jakkula, ―Ambient
intelligence: applications in society and opportunities for
artificial intelligence. Pervasive and Mobile
Computing‖,2007.
[11] Y. Zhang, K. Partridge and J. Reich, ‖Localizing tags using
mobile infrastructure‖, In Proceedings of the 3rd
International Symposium on location and context
awareness (LoCA). Lecture notes in computer Science
(LNCS 4718). Springer, (Oberpfaffenhofen, Germany,
September 20-21, 2007, 279-296.
[12]C.Wu, Y. Zhang, W. Sheng and S.Kanchi, ‖Rigidity guided
localization for mobile robotic sensor networks‖,
International. Journal of Ad Hoc and Ubiquitous
Computing, Vol: 6 (2), 114-128.
[13]A.Z.Alkar and U.Buhur, ―An Internet Based Wireless Home
Automation System for Multifunctional Devices‖, IEEE
Transaction on Consumer electronics,Vol: 51(4),1169–
1174.
