AMBIENT INTELLIGENCE PARADIGM TO CONTROL

DEVICES THROUGH MOBILE OVER WIRELESS SENSOR

NETWORKS 1

Abdul Razaque 2Khaled Elleithy

1arazaque@bridgeport.edu

2elleithy@bridgeport.edu

Wireless and Mobile Communication (WMC) Laboratory

Department of Computer Science and Engineering

University of Bridgeport

CT-06604, USA

Abstract: Rapid advancement in ambient intelligence (AI)

has attracted different walks of people. AI systems provide

robust communication in open, dynamic and heterogeneous

environments. The wireless sensor networks play fundamental

role in AI to provide automatic and multi tasking services

anytime and anywhere. In this paper, novel paradigm has been

introduced to control remotely available devices.

We deploy BT node (sensor) that offers passive and active

sensing capability to save energy. BT node works in passive

mode for outdoor communication and active for indoor

communication. The BT node is supported with novel

automatic energy saving (AES) mathematical model to decide

either modes. It provides robust and faster communication

with less energy consumption. To validate this approach, we

use two types of simulations: Test bed simulation is performed

to control mobile supported content server (MSCS) explained

in [13], [14], [15]. Ns2 simulation is done to simulate the

behavior of network with supporting mathematical model. The

main objective of this research is to access remotely available

several types of servers, laptops, desktops and other static and

moving objects. This prototype is initially deployed to control

MSCS from remote place through mobile device. The

prototype can further be implemented to handle several

objects simultaneously in university and other organizations

consuming less energy and resources.

Keyword:-BT sensor node, Device controlling, AES mathematical

model, saving energy, Ubiquitous communication.

1. Introduction

The individuals have observed scientific and technological

leaps for centuries that changed the lives. The technological

progress in smallness of microprocessors has made significant

advancement for ambient intelligence (AI) [1]. AI is the

fastest growing segment for attracting the people around the

world [5], [31], [32]. AI nourishes from many well organized

fields of computing and engineering. It also combines several

professions through many application domains, e.g., health,

education, security and social care.

Many objects 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) [2]. AI involves compact power that is

adapted to achieve specific tasks. This prevalent accessibility

of resources builds the technological layer for understanding

of AI [6], [8].

Information and communications technologies (ICT) have

highly been accepted as part of introducing new cost-effective

solutions to decrease the cost of pedagogical activities and

healthcare. For example, the Ubiquitous intelligence health

home that is equipped with AI to support people in their

homes. While this notion had some problems to be fully

understood in the past, but due to emerging technologies and

incredible progress in low-power electronics and sensor

technologies supported with faster wireless network have

facilitated the human life. Robust heterogeneous wireless

sensor network systems can be organized for controlling

mobility of objects and logistics [30].

These developments have led to the introduction of small-

sized sensors that are capable for monitoring the constraints of

humans as well as living environment [3]. The objective of AI

with use of sensors is to provide better living standard [27].

The deployment of mobile devices as sensor node provides

more flexibility to interact with objects in any environment [4].

These solutions not only improve the quality of education and

health of people in their own homes but also provide the fastest

way of communication to interact with devices all over the world

[9]. The exploitation of mobile devices in wireless sensor

network provides more flexibility, intelligence and adaptivity to

interact with devices dynamically in any environment [11]. This

makes it possible to deploy mobile phone not only as terminal

but also as remote controller for several devices. With

deployment of mobile infrastructure, larger area can be covered

as compare with installed infrastructure using same number of

sensors [10]. From other side, wireless networks face many

challenging issues including unreliable communication,

consumption of energy, storage resources, inadequate computing

and harsh environments [33].

In this paper, we introduce a novel paradigm that involves

mobiles and sensors. In particular, our goal is to introduce faster

and robust wireless sensor network to facilitate in controlling

remotely available servers and different devices. It also provides

remote accessibility with minimum energy consumption.

2. Related work

Controlling remotely available servers using mobile devices is

one of highly challenging issues because of scalability,

interoperatibility, limited services and security of mobile

devices. The access of one or multiple servers from remote

places causes of saving the resources and fostering

communication. The mobile devices supported with sensors

make the task quicker and smarter but from other side, sensors

consume more energy. No matured paradigm has been found

in literature that may indicate that devices are being controlled

remotely using the sensors and mobiles selecting robust

efficient path. The salient features of most related work are

discussed in following studies. Until now, adhoc solution has

been deployed in home automation with support of current

technologies to fulfill the requirements of users. Java based

home automation system based on internet has been

introduced [22]. Authors manage some digital input and

output lines which are connected with appliances. Internet

based wireless home automation system for multifunctional

devices have been introduced [26] to provide remote access.

The authors in [22] and [26] used radio frequency link and

simple management protocol to handle these devices.

Home automation solution for indoor ambient intelligence

(AI) has been implemented in [23]. Authors have used

gateway and local control panels to maintain security system

for house. Security involves with communication and

performs several entities same time. The IP based

communication platform has been developed to interact with

security company and security staff. Authors in [24]

introduced building automation systems to control house hold

appliances with support of typical services and standard

applications models. The paper also focused on BACnet,

EIB/KNX and Lonworks as open system in building

automation domain. Three-level working model was

implanted inside automation pyramid that reduced convolution

of individual level and kept levels transparent and lean.

3. Proposed Architecture

Our proposed architecture consists of two types of devices:

mobile phone and specific type of sensors (BT node). The

mobile phone is used to interact with different types of servers

and other devices. Initially mobile device is deployed to

control remote servers but it can further be implemented to

control several types of devices.

The BT node provides self-directed prototyping platform

based on microcontroller and Blue-tooth radio. It 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 to BT node to create multi-hop networks.

Second radio is Zeevo ZV4002 supported with Bluetooth

module.

We deploy BT node sensors in wireless sensor network to

control MSCS remotely to make pedagogical activities faster

consuming less energy through mobile. The BT node provides

multiple interfaces to control many devices at the same time

but issue is consumption of lot of energy during the sensing

time. We implement (ESSEP) model to enforce BT node to

work either in active or passive mode for saving the energy.

To make faster communication, (SEP) mathematical model is

also deployed. The BT sensors automatically turn off when

they do not participate in communication. To automate

transition mode, third mathematical model (AAS) is also

implemented.

We use different protocols and standards in our architecture.

Most of sensors do not communicate with Zigbee/ IEEE

802.15.4 standard [18]. Wi-Fi and Bluetooth are also not

compatible because both utilize unlicensed 2.4 GHZ ISM

bandwidth. So they are using same bandwidth and in resulting

causes of interference between them. In addition, both are also

transmitting data at binary phase shift keying (BPSK) and

quadric phase shift keying (QPSK).

Our selection of BT node sensors provides the compatibility to

Zigbee, Bluetooth and Wi-Fi. It supports different types of

applications and having multitasking support. Figure1 shows

simple architecture for WSN applications.

BT NODE SENSOR

SENSOR

UART

DRIVERS

12CGPIORTCAD

CONVERTER

COMMUNICATION TECHNOLOGY

IEEE 802.15.4 ZIgBee Bluetooth

APPLICATION

JOB-NJOB-VJOB-IVJOB-IIIJOB-IIJOB-I

Figure 1: Architecture for Wireless sensor application

. 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. We have discovered

several theories that enabled to make mobile phone

compatible with 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 best platform to interlink the

mobile phones with sensors for establishment of connectivity

to control remote access devices. We deploy highly featured

hybrid network shown in figure 2. This multi-featured network

supports to all BT node sensors and different product of

mobile families supported with Zigbee/IEEE 802.15.4 and

IEEE 802.11 b/g/n standards. Sensors select shortest efficient

path on basis of used mathematical model that choose reliable

path for sending the data. In addition, on the sensor network,

only path- finder sensors are active and remaining sensors are

always at sleep state.

MSCS

Wi-Fi ACCESS POINT (AP)

STREAMING SERVERIE

EE

802.1

5.4

/

Zig

Bee

IEE

E 8

02.1

5.4

/

Zig

bee

IEEE B

02.1

1

b/g/n

BT NODE

SENSOR

BT NODE

SENSOR

CONNECTED

WITH WI-FI

THROUGH USB

REGION-1

REGION-2

REGION-3

REGION-4

Internet

SMART DEVICE

(MOBILE)

WITH BT NODE

(SENSOR)

BACKBONE NETWORK

WLAN

LOG

ICAL C

ONNECTIO

N

WL

AN

BT NODE

SENSOR

BT N

ODE

SENSO

R

EFFICIENT

SHORTEST

PATH

BOUNDARY

NODE

Figure 2: Proposed wireless sensor network to control MSCS through mobile device

The decision of going into sleep and active mode is done

with AAS mathematical model. It decides either sleep or

active transition. The beauty of sensor network 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 SEP

mathematical model. Participating sensors go

automatically into active and passive modes for saving the

energy. This process is also done with AES mathematical

model for consuming and gaining energy in active and

passive modes. The beauty of AAS model is to enforce

sensor to go into either OFF or ON transition without

waiting any directions when task is done. Even the

sensors that are part of the sink after delivering the data

go automatically into ‘Off’ transition state. The only

boundary sensor is active and playing role for

communicating with boundary node or another node of

next region. Collective working process of mathematical

models make network smarter to carry the commands of

mobile devices in faster way to control remotely available

servers and other devices.

3.1 Automatic energy saving (AES) model to

activate passive and active mode of BT node

Assume number of sensors are N, which are deployed to

detect the presence or absence of indoor and outdoor

environment (IOE). Sensor K collects information

pertaining to IOE and makes decision. Di (Di =1 for

deciding presence of indoor environment (IE) and

otherwise Di =0, it means outdoor environment (OE). The

detection process is based on maximized probability of

detection (PD), with respect to constraints on probability

of unknown environment (UE), and by Neyman-Pearson

Lemma [16].[19],[20]. Thus, the IOE environment is

obtained. With help of blinking observations, Neyman-

Pearson Lemma explains optimal detector design of

detector but we map this to map for IOE environment. In

other words, the sensor detects environment with

realization of random variables K, UE and PD. Since

probability of unknown environment (UE) is random

variable, which explains constraint of optimized problem

in form of UE= α is no more applicable. One important

selection for statistical optimized quantity is the expected

value of UE and PD. Hence, we maximize expected value

of probability to detect UE, with respect to constraints of

expected value of probability [28].

Max E (PD) and E (UE) = α

∑ &

∑ : Hence,

are linked by relative operating

characteristics (ROC) curve, we assume the functional

form of relationship is according to f( . Thus

∑ (

)

∑

(2)

The Problem of optimization of constraint can be defined

as follows:

Max E(PD) and E (UE) = α

0 ≤ ≤ 1, k = { 0, 1, 2,…,} Here, is vector of

realization of unknown environment

( ) …]

In realistic environment, the UE cannot be infinite, since

probability of getting many number of samples of

environment, P ( k=k) during finite T, reaches 0 as K →

∞, for any available environment.

Lagrangian multiplier method can be used to resolve these

issues as follows:

{ α] ∑ ( )

∑ ( ) α

Distinguishing with respect to , and equating to 0, So

we obtain:

( )

( ) ∞

This can be further expressed as:

∑ ( )

𝐾 ∞

𝛼 4

K is sensor node of N that is case of finite capacity

environment, then equation (3) represents N equations in

, and combine with equation (4). Thus, we can

resolve N +1 by using , , where k ={ 0, 1, 2, 3…,

N}. We can obtain the threshhold of UE using inverse of

relationship of . Therefore, we can obtain unknown

environment but we have also to find indoor and outdoor

environment. We are using following probabilities for

indoor and outdoor environment.

UEi = P (Di =0 | IOE outdoor), i = P (Di0 = 0| IOE

outdoor), PDi = P (Di = 1| IOE indoor), i = P (Di0 =1|

IOE indoor). Assume that detection of environment is

independent made by sensor, the UE probability of

obtained decision from ith sensor is given by following

equation:

Here Pc1(Pc0) is probability of IOE. When sensor ith

transmits bit for finding “0” and “1”. For IOE, Pc1 = Pc0

= Pc; Therefore, (5) can be simplified as follows:

The probability of detection the IOE depends on receiving

decision can be obtained from (6) and replacing UE with

PD. FOR IOE

This helps to determine the IOE, on basis of decision, BT

node automatically works either active or passive mode.

This automatic process of detection causes the saving

energy. We replace with IOE.

are showing the nature of environment,

; we substitute with

Di. We get: If we obtain the value of Di, replace probability detection

(PD) with its substitute values.

∑ ( ) ; Now

rearrange and get Di.

∑ ( )

If we obtain the value Di =1, it means passive mode is

initiated and sensor saves the energy. Passive mode is

supported by energy of sun. Di=0 gives the sign of active

mode, and BT node consumes the energy in this mode,

which obtains through passive mode. If Di ≥ 1 || Di ≤ 0,

shows that environment is unknown and sensor does

not work and goes to sleep position in order to save the

energy.

We can obtain energy preserving as follows:

∑

∑

Where EN(X) and EN(Y) denote total energy used by two

different networks. E(i,j) indicates the energy used by

node i and j during transmission.

Assume that aij is different from 0, only if node j receives

E(i,j) when node i transmits. This gives appropriate

minimum level Emin i.e. if

We incur from the equation (13), it consume less energy

during the process.BT sensor node follows the energy

saving integration method during the passive process. We

here show numerical time integrators that causes of

preserving energy P(e), we begin by assuming an x-point

quadrature formula with nodes Ni. The required weight of

ai is obtained through Lagrange basis polynomials in

interruption that is shown as follows:

∏

∫ 4

Let a1, a2, a3,…ax be different real numbers (usually 0 ≤

Ni ≤1) for which ai ≠ for all i. we use polynomial p(d0) for

satisfying the degree.

∫

The quadrature formula with nodes Ni and weights ai

decreases integrator to specific collection of methods. We use

polynomial degree 2x – 1, thus Gauss points Ni that is

equal to 0 and shifted with Legendre polynomial specific

collection for A(x). This treats arguments in A(x) and

with different way that is considered as partitioned

numerical method. The solution of these methods depends

on specific factorization of vector filed. Assume, If A(x)

= A is constant matrix, let (1, 1) be Hamiltonian system,

thus it becomes energy saving integrator of [23]. This is

proof that sensor node also consumes minimum amount

of energy and saves the energy in passive mood. 4. Simulation setup 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 differing extensively from realistic experimental

results, if network simulator makes only simple

assumptions on wireless radio propagation [25]. Exact

simulation to the features of real wireless radios with

diverse transmission powers is significant for assessing

the realistic performance of any new introduced model

and algorithm in wireless sensor network. For our

experimental simulation setup, we use ns-2.35 RC7. AES

model provides baseline for topology because this model

provides the capability to sensors to sense the

environment. On basis of sensing process, sensors are

triggered either remain in active or passive mode.

The wireless sensor network 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 three types of different scenarios: First

scenario only controls single server from remote access

and consisting of two BT node sensors. One sensor is

connected with mobile device and other sensor is

connected with server, we do not examine the

performance of used mathematical model in the first

scenario.

First scenario is done with testbed simulation as well as

ns2 based simulation. Next two scenarios are done with

only ns2. In second scenario, we control multiple servers

using multiple mobiles from remote access. This is

congested scenario and all four regions, serving their jobs

but we have tested the performance of wireless sensor

network (WSN) in this scenario. Our objective in this

scenario is to interact with remote servers and other

objects. Third scenario is particularly designed to examine

the behavior of three deployed mathematical models,

performance of BT node sensors and capacity of network.

This scenario is real test of WSN. We have deployed 70

sensors in last two scenarios within network area of 160m

× 160m. Area is divided into 40m x 40m regions. Sensors

are randomly located within each region. Nodes onto sink

are randomly chosen with SEP. The sink in first scenario

is located at (140, 60) but remaining two scenarios,

having many sinks because multiple mobile devices are

using WSN and getting remote access to several servers

and devices. The bandwidth of node is 50 Kbps and

maximum power consumption for each sensor was 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 35 minutes and there is no pause

time during the simulation but we 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. The results shown in this section are

average of 10 simulation runs.

4.1 Efficiency of Network

With objective of validating this novel environment of

WSN for handling servers and other several devices, we

conduct several tests from different perspective. Having

proved the mathematical models with supportive

definitions, lemmas and equations, let us now evaluate the

coverage efficiency of network. We have already

discussed about area of network that is 160m × 160m and

divided into 40m x 40m regions. We target coverage

efficiency of network after deploying from 1 to 70 sensors

shown in figure 3.

CO

VER

AG

E EF

FIC

IEN

CY

OF

NET

WO

RK

0

0

0.2

0.4

0.6

0.8

1

10 20 30 40 50 60 70

TRADITIONAL

METHOD

ESSPM

NUMBER OF SENSORS

Figure 3. Showing the coverage efficiency of WSN

Our simulated network shows that our proposed

mathematical models achieve almost 100% efficiency

whereas general wireless sensor network gets 69%

efficiency using 70 sensors. We establish 15 sessions

simultaneously in order to determine the actual behavior

of network in highly congested environment. If we have

less number of sensors, it is hard to establish many

sessions at the same time. So, it is very interesting

direction of this research that 70 sensors can provide path-

connectivity for 15 mobile devices to interact with

remotely placed devices at same time. In addition, one

mobile can interact with multiple devices at same time.

Question is why to deploy more sensors in that area? And

logically answer is availability of several servers and

devices at the different places.

CO

VE

RA

GE

EFF

ICIE

NC

Y O

F N

ETW

OR

K

0

0

0.2

0.4

0.6

0.8

1

5 10 15 20 25 30 35

ESSPM

TIME (MINUTES)

TRADITIONAL

METHOD

Figure 4. Coverage efficiency of network at different time

More sensors are required to find path and provide the

connectivity for enough number of mobiles working

concurrently. Figure 4 shows the trend of network during

the time of 35 minutes of simulation at the same number

of established sessions. During this duration, the mobiles

get 99.2% coverage of network whereas traditional

method affects the performance of network as time

increases of communication. This is another weakness of

traditional method. Our proposed method produces stable

efficiency during all the simulation. 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: Nmin(s) is minimum number of sensors to

cover whole area to maintain connectivity and coverage.

‘r’ is sensing range of sensor and it should be assumed

that sensing range is smaller than dimensions of

monitoring area.

is maximum number of sensors.

‘R’ is distance of total network. We prove this with help

of lemma4.

Lemma 4:

is upper bound on R and Nmin(s) is

lower bound on Si, where Nmin(s) =

.

Proof: Let upper bound be linear on R with maximum

number of sensors (total number of sensors) Nmax(s)

whereas lower bound on Si is invariant with Nmax(s). In

addition, these bounds are not considered tight as long as

they do not consider transmission radius ‘Tr’ of sensors.

However, we need better heuristic solution to follow these

bounds closely if irrespective of changes occur in the

parameters of network. Hence, life time of network

should linearly be with Nmax(s), and Si to be constant with

Nmax(s).

4.2 Maximum Path detection time

Routing in wireless sensor networks is moderately

multifarious due to various limitations. Our goal is to find

faster shortest path despite of many constraints. In this

experiment, we want to prove if numbers of hops are

increased, how SEP mathematical model supports in

finding faster route. The trend of our proposed and

traditional network is shown in figure 5. Simulation

parameters are same that are discussed in section 6.

Approach of our SEP model is to store information about

only 1-hop destination node. It helps to provide the

quicker information about whole network and causes of

energy saving as compare with tradition network. In this

experiment, we have maximum 21 hop-destinations and

15 sessions are established to access the remotely

available servers and devices. If, we analyze the figure 6,

it is proved that time for maximum number of hops is

calculated with 0.8 seconds in our case whereas it takes

2.25 seconds for traditional WSN.

Our approach produces 281.25% better performance than

traditional WSN. In addition, advantage of finding faster

shortest path in multi-hop wireless sensor network is to

maintain scalability and simplicity, Straight line routing

(SLR) is approximated for measuring shortest path

destination in massive multi-hop wireless networks [29].

SLR is defined as series of nodes whose boundary cells

are cut with SLR between source and destination [12]. In

our case, the search is only 1- hop destination. Path

detection time for each hop is varied because it depends

on the probability of density function for each node that is

calculated as follows:

]

is probability density function, H [Nr] is number of

hops in network, is length of network. The value of

] is given in [29].

Hence, ]

arcsin (r) (2)

Here ‘r’ is distance from source‘s’ to destination ‘d’.

Substitute the value of ] and put in (1)

4

Simplifyin (3), we get:

4

Where, is Time for maximum number of

hops, ) is total area of network and is corresponding

velocity of each node. Substitute the values of and

we get:

4

∫

Substitute value of in (6), we get:

4

∫

PA

TH D

ETE

CTI

ON

TIM

E (S

econ

ds)

0

0

0.5

11.

52

2.5

3 6 9 12 15 18 21

ESSPM

NUMBER OF HOPS

TRADITIONAL

METHOD

Figure 5. Path detection time for different number of hops

4.3 Consumption and Saving Energy

The primary goal of this research is to consume less

energy during the communication between mobile devices

and remotely placed servers and several objectives.

Maximizing the life time of WSN, mathematical models

help to consume minimum energy The advantage of this

research is to automate the BT node sensors in active and

passive mode that cause of saving energy in whole

network. In addition, selection of SEP and 1-hop

destination approach get the minimum energy consumed.

We use same parameters explained in section 6 but in this

experiment, we have established 30 connections and

transferred 29 GB data at the rate off 377 *10-6

seconds/byte.

Total numbers of sensors have consumed 151 joule

energy in our case, whereas tradition wireless sensor

network has consumed 500 joule shown in figure 6.

CO

NS

UM

ED

EN

ER

GY

(Jou

le)

0

0

100

200

300

400

500

10 20 30 40 50 60 70

ESSPM

NUMBER OF SENSORS

TRADITIONAL

METHOD

Figure 6: Energy consumption at different number of

sensors

It is validated through simulation that our approach has

saved 87.2% energy shown in figure 7.

TOTA

L S

AV

ED

EN

ER

GY

%

0

0

2040

6080

100

10 20 30 40 50 60 70

ESSPM

NUMBER OF SENSORS

Figure 7.Total Energy saving for number of sensors

Tradition WSN has consumed 331.12% more energy than

our approach. Energy consumption is calculated as

follows. Transmission of generated data in whole network

can be obtained as:

∫

Generation speed of data in is obtained as:

∫

Where, ‘Ds’ is speed of generated data in whole network

that is constant K.

Total energy consumption speed in region-N can be

calculated as:

∫

Thus, total consumption of energy Tc is deduced to

combine (9), (10) and (11), we get:

Tc ∫

∫

∫

CONCLUSION In this paper, we have introduced energy saving and

shortest efficient path driven paradigm that provides

access to control remotely available servers and several

types of devices through mobiles. This is purely unique

type of research that deploys BT node sensors in wireless

network to control the devices. We have introduced

automatic energy saving (AES) model that senses the

environment to activate either passive or active mode of

BT node sensors for saving energy.

The advantage of this model is to activate only one sensor

in whole region. When sensor finishes the job, it

automatically goes to sleep mode. To validate the

proposed paradigm in WSN, we implement proposed

mathematical model in ns2.35-RC7. On the basis of

findings, we prove that our proposed research have saved

maximum amount of energy as compare with traditional

method of saving energy. In addition, we have achieved

objectives to control the remotely available servers and

devices by consuming minimum energy resources.

In future, we will implement this simulation based work

into testbed to control several devices simultaneously. In

addition, we will implement the applications of this

contribution in ambient intelligence to control house

automation devices, office devices and several static and

moving devices using mobile.

ACKNOWLEDGMENTS

This work is funded by wireless and mobile

communication (WMC) laboratory and with coalition of

AT & T. We also thank for Varuan Pande for his

insightful discussion on this work.

REFERENCES

[1] J. Chin, V. Callaghan, and G. Clarke. 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.

[2] Diane J. Cook, Juan C. Augusto, and Vikramaditya

R. Jakkula. Ambient intelligence: applications in

society and opportunities for artificial intelligence.

Pervasive and Mobile Computing, 2009.

[3] H. Nakashima, A. Aghajan, and J.C. Augusto,

editors. Handbook on Ambient Intelligence and

Smart Environments. Springer Verlag, 2009.

[4] S. Reddy, Vids Samanta, Jeff Burke, Deborah Estrin,

Mark Hansen, Mani B Srivastava, MobiSense -

Mobile Network Services for Coordinated

Participatory Sensing, Proc. of the 9th International

Symposium on Autonomous Decentralized Systems

(ISADS), 2009.

[5] Chang, B., Lim, M., Ham, D.-H., Lee, Y., Kang, S.,

& Cha, J, “Supporting Multi-device for Ubiquitous

Learning”, Lecture Notes in Computer Science,

4469, 457-467, 2007.

[6] Chiu, D. K. W., Choi, S. P. M., Wang, M., &

Kafeza, E, “ Towards Ubiquitous Communication

Support for Distance Education with Alert

Management”, Educational Technology & Society,

11 (2), 92-106, 2008.

[7] Kajita, S., & Mase, K. (2006). uClassroom:

Expanding Awareness in Classroom to Ubiquitous

Teaching and Learning. Proceedings of the 4th IEEE

International Workshop on Wireless, Mobile and

Ubiquitous Technology in Education (pp.161-163),

Los Alamitos: IEEE Computer Society.

[8] Milrad, M., & Spikol, D. (2007). Anytime,

Anywhere Learning Supported by Smart Phones:

Experiences and Results from the MUSIS Project.

Educational Technology & Society, 10 (4), 62-70.

[9] Jürgen Bohn, Vlad Coroamă, Marc Langheinrich,

Friedemann Mattern, Michael Rohs, “ Social,

Economic, and Ethical Implications of Ambient

Intelligence and Ubiquitous Computing”, article is

based on an earlier journal article “Living in a World

of Smart Everyday Objects – Social, Economic, and

Ethical Implications”, Human and Ecological Risk

Assessment, Vol. 10, No. 5, October 2004.

[10] Zhang,Y., Partridge,K., Reich,J.2007.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.

[11] Wu, C., Zhang, Y., Sheng, W., Kanchi,

S.2010.Rigidity guided localization for mobile

robotic sensor networks. International. Journal of Ad

Hoc and Ubiquitous Computing. 6 (2), 114-128.

[12] Sungoh Kwon and Ness B. Shroff, “Paradox of

Shortest Path Routing for Large Multi-Hop Wireless

Networks”, Center for Wireless Systems and

Applications (CWSA), School of Electrical and

Computer Engineering, Purdue University, 2006.

[13] Abdul Razaque and Khaled Elleithy

"Architectured based Prototypes for Mobile

Collaborative Learning to improve Pedagogical

activities" Interactive collaborative learning (ICL),

IEEE, Piešany, Slovakia, September 21 – 23, 2011.

[14] Khaled Elleithy and Abdul Razaque ,"Innovative

usability testing to foster mobile applications to

support collaborative learning environment

(MCLE)".Journal of International Conference on

Computer Science and Engineering (CSE-11)

ICGST, 2012.

[15] Abdul Razaque and Khaled Elleithy, "Interactive

linguistic prototypes to foster pedagogical activities

through mobile collaborative learning

(MCL)"International Journal of Interactive Mobile

Technologies (iJIM)",Vol 5, No 3 (2011).

[16] Q. Fang, J. Gao, L. J. Guibas, V. de Silva, 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), pp. 339-350.

[17] Chiasserini. C and Rao, R, “Improving energy

saving in wireless systems by using dynamic power

management”, IEEE Transactions on Wireless

Communications 2 (5), 1090–1100, 2003.

[18] P. Barontib, P. Pillaia, V. W.C. Chooka, S.

Chessab, A. Gottab, Y. F.Hua, Wireless Sensor

Networks: A Survey on The State of The Art and the

802.15.4 and ZigBee Standards, Computer

Communications, Volume 30, Issue 7, May 2007, pp.

1655-1695.

[19] Ioannis Chatzigiannakisa,b, Athanasios Kinalisa

,b, Sotiris Nikoletseasa, ”Fault tolerant and efficient

data propagation in wireless sensor networks using

local , additiona network information”, Elsevier

International Journal of Parallel and distributed

computing 67 (2007) pp-456–473, 2007.

[20] S. M. Kay, Fundamentals of Statistical Signal

Processing, Volume 2:Detection Theory, ser. Prentice

Hall Signal Processing Series, A. V.Oppenheim,

Ed.Prentice Hall PTR, 1998.

[21] Hairer, E.: Energy-preserving variant of

collocation methods. JNAIAM J. Numer. Anal. Ind.

Appl.Math. 5, pp-73–84, 2010.

[22] A.R,Al-Ali and M.Al-Rousan,“Java-Based

Home Automation System,”IEEE Trans .Consumer

Electronics, vol..50, no.2, 2004, pp. 498–504.

[23] Antonio F. Gómez-Skarmeta, “An Integral and

Networked Home Automation Solution for Indoor

Ambient Intelligence” IEEE Trans. on Pervasive

computing, 2010,pp. 1536-1268.

[24] Wolfgang Kastner, Georg Neubschwandtner

Stefan Soucek and H. Michael

Newman,“Communication Systems for Building

Automation and Control”, Proceedings of the IEEE,

VOL. 93, NO. 6, JUNE 2005, pp.1178-1203.

[25] Jerry Zhao and Ramesh Govindan,

“Understanding packet delivery performance in dense

wireless sensor networks,” in Sensys), Los Angeles,

CA, November 2003.

[26] A.Z.Alkar and U.Buhur,“An Internet Based

Wireless Home Automation System for

Multifunctional Devices”, ”IEEE Trans. Consumer

electronics ,vol.51,no.4,2005, pp. 1169–1174.

[27] L. Benini, E. Farella, and C. Guiducci, “Wireless

sensor networks: Enabling technology for ambient

intelligence,” Micro electron Journal, vol. 37, No. 12,

pp. 1639–1649, 2006.

[28] Waltenegus Dargie, Christian Poellabauer,

“Fundamentals of wireless Sensor Networks”, Wiley

Series on Wireless Communications and Mobile

Computing, 2010.

[29] P. Gupta and P. R. Kumar, “The capacity of

wireless networks,” IEEE Transactions on

Information Theory, vol. 46, no.2, pp. 388–404,

March 2000.

[30] Vermesan.O, “Wireless smart systems beyond

RFID” European Conference & Exhibition on

integration issues of miniaturized systems MEMS,

MOEMS, ICs and electronic components, 9-10 April

2008, Barcelona, Spain.

[31] Purhonen, A., Tuulari, E., “Ambient Intelligence

and the development of embedded system software”,

Academic Publishers. pp. 51-67, 2003.

[32] Jorg Cassens,” Explanation awareness and

ambient intelligence as social technologies”, Thesis

for doctor degree, Norwegian University of Science

and Technology, 2008.

[33] L. Evers, M. J. J. Bijl, M. Marin-Perianu, R.

Marin-Perianu, P. J. M. Havinga,” Wireless Sensor

Networks and Beyond: A Case Study on Transport

and Logistics”, work was partially funded by the

projects EYES (IST-2001-34734), Cobis (IST-2004-

004270), and Smart Surroundings, 2005.