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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.
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1Abdul Razaque 2Khaled Elleithy
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
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
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.
IEEE 802.15.4 ZIgBee Bluetooth
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.
IEEE 802.15.4/
IEEE B02.11
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
       
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
          
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 
 & 󰇛󰇜
 : 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 
       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
     
Lagrangian multiplier method can be used to resolve these
issues as follows:
Distinguishing with respect to , and equating to 0, So
we obtain:
This can be further expressed as:
 󰇛󰇜
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        
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       i0 = 0| IOE
outdoor), PDi = P (Di     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
Here Pc1(Pc0) is probability of IOE. When sensor ith
 
= 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
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. W
 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       
shows that environment is unknown and sensor  does
not work and goes to sleep position in order to save the
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:
󰇛󰇜 
 󰇛󰇜󰇛󰇜
Let a1, a2, a3x    
i 0) 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
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.
0.2 0.4 0.6 0.8 1
10 20 30 40 50 60 70
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.
0.2 0.4 0.6 0.8 1
5 10 15 20 25 30 35
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.
          
that sensing range is smaller than dimensions of
monitoring area. 󰇛󰇜
󰇛󰇜 is maximum number of sensors.
  
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
       r  
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
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)
 
Substitute the value of 󰇟󰇠 and put in (1)
 󰇛󰇜
Simplifyin (3), we get:
 󰇛󰇜
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:
 󰇛󰇜
 󰇛󰇜
 󰇛󰇜
Substitute value of 󰇛󰇜 in (6), we get:
 
 󰇛󰇜
0.5 11.5 2 2.5
3 6 9 12 15 18 21
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
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.
100 200 300 400 500
10 20 30 40 50 60 70
Figure 6: Energy consumption at different number of
It is validated through simulation that our approach has
saved 87.2% energy shown in figure 7.
20 40 60 80 100
10 20 30 40 50 60 70
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, 
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:
  
 
 󰇛󰇜
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.
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.
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