1. 1
Cloud Based Active Health Monitoring with an Optimal
Communication Scheme
Jesna fathima,PG Scholar
Department of computer science and engineering
Vemana institute of technology,Bangalore
jesnajaseem@gmail.com
Abstract: Cloud integrated with wireless sensor
networks focuses on push-pull communication
known as sensor cloud Integration platform as a
service (SC-iPaaS).It is a three layer architecture
which consists of sensor, edge and cloud layers.
The sensor layer consists of wireless body sensors
networks.The edge layer consists of sink nodes
that collect sensor data from sensor nodes .The
cloud layer consists of cloud application that
obtains sensor data through sink nodes in the edge
layer. An optimal data path is formulated for
individual sensor and edge nodes. A simulation
environment is also set up that performs remote
multipatient monitoring with five on-body sensors
including ECG, pulse oxiometer and
accelerometer per patient.
Keywords: Cloud, virtual sensors
INTRODUCTION
The Cloud-integrated architecture which is used
in wireless sensor networks acts as a communication
optimizer for the system. The developed system,
called Sensor-Cloud Integration Platform as a Service
(SC-iPaaS) is a three-tier communication architecture
that will combines the sensor, edge and cloud layers.
The sensor layer posses of sensor nodes sandwiched
in the physical environment.
SC-iPaaS will implements a push-pull hybrid
communication in between the layers. Individual
sensor nodes periodically transmit sensor data to sink
nodes, which again forward this incoming sensor data
periodically to virtual sensors. When a virtual sensor
does not have sensor data that a cloud application
requires, it pulls that particular data from the sink
node. This push-pull communication mechanism is
mainly developed to make as much sensor data as
possible for cloud applications by taking the benefit
of push technique by allowing virtual sensors to pull
any missing data at anytime in an on-demand.
The proposed SC-iPaaS can be implemented
on home based patients. The application assumes per-
patient wireless networks of on-body and/or in-body
sensors for, for example, heart beat count, blood
pressure, oxygen saturation, body temperature,
respiratory rate, blood coagulation, galvanic skin
response and fall detection values. At a time it can
deploy at least five on body sensors to the patient
body.
In 2009 Hassan,Biao and Nam huh has proposed a
framework of sensor-cloud by clubbing
opportunities and its challenges. They made a three
tier architecture which was similar to the proposed
SC-iPaaS and determined the publish subscribe
communication between edge layer and cloud layer .
2. 2
The physiological and activity sensor data
are periodically given to virtual sensors present in
clouds so that doctors, nurses and visiting nurses can
access the data for clinical study and intervention.
When any abnormity is detected in the sensor data,
clinical staff may pull an extra data in a greater
resolution to clearly determine the patient’s medical
situation. while a sufficient amount of data is
obtained, they will perform clinical interventions,
such as dispatching of the ambulances , notify the
family members of patients. The proposed system
focuses on a communication optimization problem in
SC-iPaaS and solves it with an optimization
algorithm. The problem is to find an optimal data
transmission rate for the sensor node as well as sink
node with respect to multiple optimization objectives
such as sensor data availability,band-width and the
energy consumed. Here a simulation environment is
also considered that performs remote multi-patient
monitoring with five on-body sensors. Simulation
results shows that the proposed optimization
algorithm is getting a pareto-optimal communication
configurations against data request patterns of the
cloud applications The architecture posses three
layers that are sensor layer,cloud layer and the edge
layer as shown in the figure 1.
Sensor layer:- This layer consists of one or more
wireless networks. Each network is considered as
desperate. It possess heterogeneous sensor devices
such as air temperature sensors, humidity sensors and
barometric pressure sensors. Sensor nodes are either
battery operated or solar powered,so they have very
less energy supplies. Each sensor nodes form a
particular network..In fig 1 sensor nodes is using a
tree topology. Nodes will periodically collects the
sensors and push this sensor data to the sink node on
a hop by hop manner through tree topology. Different
sensor nodes have different transmission rates.
Fig 1: Architecture for cloud sensor network
Edge layer:- It is cluster of sink nodes ,each of them
will cooperate with the sensor networks and receives
sensor data regularly from each nodes in the
network. Every sink nodes caches the incoming
sensor data in its memory space and then pushes
3. 3
them to the cloud layer. It preserve the mapping
between physical sensors as well as virtual sensors. It
has knowledge about the root and target of sensor
data. Different sink nodes have different transmission
rates. The data transmission rate for the sensor node
will be different from the of sensor nodes in the same
network. In figure 1 one sink node is operated in each
sensor networks. Depending up on the application
domains, sink nodes possess less energy supplies.
In addition to the push operation of the
virtual sensor node, all the sink node accepts a pull
request from a virtual sensor when it lacks that data
in the cloud. if the sink node has the data being
requested in its space ,it provides that data Otherwise
it will issue a pull request to the sensor node which is
intended for the requested data. After getting the pull
request, the sensor node reads out a sensor and
returns with the sensor data.
Cloud layer:-This layer is capable to operate on one
or more clouds. Application can be deployed on the
virtual machines available in the cloud. The cloud
users will give the queries to the cloud application
that is present in the cloud. The virtual sensor checks
whether it has the data requested by the user, if so it
will returns the data. If a query is not matching, then
the virtual sensor can send the pull request and in
turn send back to sink node.
The Cloud based management services is
offering the common functionalities to implement
and operate applications. It focuses basically on two
services. They are sensor management and the
communication management.
Sensor manager: This is responsible to hide low level
operational details from the users. Cloud application
normally access the physical sensors using the virtual
sensors ,collecting the sensor data with a pull request
and sending control signals is a of the sensor
manager.
Communication manager:As the name implies it is
responsible for the optimal communication between
its layers. It’s function is to perform a push-pull
hybrid communication between various layers. It is a
considered to be operated on the top of certain
publish/subscribe communication middleware such
as TinyDDS [4]. The major component of this
manger is communication optimizer.
In 2008 Boonma and Suzuki proposed that the
TinyDDs is an interoperable and configurable
publish/subscribe middleware for the various wireless
sensor networks. The Wireless sensor networks
(WSNs) are used to detect the events and/or collect
data in several domains such as environmental
observation, structural health monitoring, human
health monitoring, inventory tracking, home/office
automation and military surveillance.
Communication optimization problem in SC-
iPaaS
The major optimization problem inorder to determine
the pareto -optimal data transmission rates for sensor
and sink nodes in SC-iPaaS is described using the
following notations.
• S = {s1, s2, ..., si, ..., sK} is the collection of
K sensor nodes and si denotes the data
transmission rate for the i-th sensor node (si)
4. 4
to push sensor data to the sink node. The
rate is measured as the number of sensor
data transmitted per a unit time. di indicates
the size of single sensor data that si
generates and transmits to a sink node. Hi
denotes the shortest logical distance (i.e.,
hop count) from si to its corresponding sink
node.
• Vei denotes the data transmission rate for a
sink node to push sensor data receiving from
the i-th sensor node.
• Ri = {ri1, ri2, ..., rij, ..., ri|Ri|} is the set of
all sensor data requests that cloud
applications issues during the time period of
W in the past.
• Rei ∈ Ri is the set of sensor data requests for
which the virtual sensor s′ i has no data.
• Rsi ∈ Rei ∈ Ri is the set of sensor data
requests for which the sink node for si do
not have data.
Three optimization objectives used here are : band-
width consumption (fB), energy consumption (fE)
and data yield for cloud applications (fD). The first
two objectives should be minimized whereas the third
is to be maximized.
The bandwidth consumption objective (fB) is defined
as the total amount of data transmitted per a unit time
between the edge and cloud layers. This objective
impacts the payment for bandwidth consumption
based on a cloud operator’s pay-per- use billing
scheme. fB is computed as follows.
( × ) +
1
(∅ × + )
∈
The energy consumption objective (fE) is
defined as the total amount of energy that sensor
nodes consume for data transmissions during the time
period of W. This objective impacts the lifetime of
sensor nodes and sensor networks. It is computed as
follows.
ℎ × × × ×
The data yield objective (fY ) is defined as the total
amount of data that cloud applications gather for their
users. This objective impacts the informedness and
situation awareness for application users. It is
computed as follows.
= ∅
∈
The data yield objective may conflicts with
the other two objectives.
SC-iPaaS consider two constraints in its optimization
process.The first constraint is the upper limit(Ce) of
energy consumption(Fe)
Fe > Ce
The constraint violation in energy consumption(Ge)
is computed a is follows where I=1,if Fe> Ce
otherwise if I=0
Ge = I × (Fe - Ce)
The second constraint is the lower limit(Cy) for the
data yield(Fy). Ie, Fy>Cy
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Optimization process in SC-iPaaS
The algorithm shows the evolutionary
optimization process in the SC-iPaaS.This will
initially distributes weight vectors V are generated
uniformly. This vector generation is executed only
once.
Algorithm: Optimization process in SC-iPaaS
1.g=0
2.Generate uniformly weight vectors and distribute
3.Pg-initializepopulation(£)
4.While g < gmax
5.Og=∅
6.While|Og|<£ do
7. C1=binary tournament operator(Pg)
8. C2=binarytournament operator(Pg)
9. If random() ≤ Pc then
10. {o1,o2}=crossover(p1,p2)
11. If random()≤ Pm then
12. C1=mutation(o1)
13. End if
14. If random() ≤ Pm then
15. O2=mutation(o2)
16. End if
17. Og={o1,o2}∪ Og
18.End if
19.End while
20.Rg=Pg ∪ Og
21.Calculate the fitness of each individuals
22.While|Rg|>£ do
23. x*=arg min ∈ry F(xi)
24. Rg=Rg{x*}
25. Update the fitness of each individual value
26.End while
27.g=g+1
28.End while
In the first generation (g=0),£ individuals
are randomly generated as the initial population Ƥ0.In
each generation a pair of individuals called parents
are chosen from the current population with a binary
tournament operator. This operator randomly
generates two individuals from Ƥg, that was then
compared with the constraint binary R2 indicator.
The constraint binary indicator can accept two
individuals and finally determines which one is
superior.
SIMULATION EVALUATION
A simulation is done in order to perform remote
physiological and activity monitoring basically for
10 patients. Every single patients patient have a sink
node and bears upto five different sensors. One sink
node and five sensor nodes form a multi-hop wireless
body sensor network. The Cloud applications are
generally simulated for issueing 10000 sensor data
request per a day. These request are then distributed
uniformly over 50 virtual sensors
The proposed optimizer is configured with a set of
parameters shown in Table 1
Table 1: Simulation Configurations
Parameter Values
Total number of sensors(S) 50
Total number of data requests 10000
Simulation time(T) 1 day
Weight of vectors 100
Reference point (0,0,0)
Population size 100
Max generations 250
Crossover rate 0.9
Mutation rate 0.01
Amplification coefficient 0.005
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Table 2 shows five types of sensors used in
simulations.10 sensors are used for each ECG data in
128 bytes. The time window for each ECG data
request is randomly generated by considering a
normal distribution with the mean of 60 seconds and
standard deviation of 10 seconds
Table 2: Configuration for sensors and sensor
data requests
Sensor used quantity Data size Time window
ECG 10 128 N(200,50^2)
Pulse oxiometer 10 15 N(40,10^2)
Accelerometer 10 100 N(40,10^2)
Body
temperature
10 10 N(600,100^2)
Blood pressure 10 10 N(300,80^2)
Type equation here
4.2 SIMULATION RESULTS
The Figure 2 shows how individuals evolve as the
number of generations grows when no constraints are
specified: CY = 0 and CE = '. It uses the hyper
volume metric [12], which measures the union of
volumes that individuals dominate in the objective
space. Thus, the hyper volume metric quantifies the
optimality and diversity of individuals. A higher
hyper volume shows that individuals are more near to
the Pareto-optimal front and more diverse in the
objective space
Figure 2.a Hypervolume measure over generations
without using constraints
Figure 2.b Hypervolume measure over generations
using constraints
Fig 4.2: Average objective values over generations
.
CONCLUSION AND FUTURE WORKS
This paper focuses on the cloud based body
sensor networking system which is called SC-iPaaS, .
SC-iPaaS consists of three layers. These layers are
responsible for the push pull communication. A
7. 7
multiobjective optimization algorithm (EMOA) was
developed to obtain the optimal communication
between the layers. SC-iPaaS generally considers
multiple objectives such as datayield,bandwidth
consumption and energy consumption and using
these it successfully optimizes data transmission
configurations. It considers a simulation environment
and determines that the proposed optimizer in SC-
iPaaS outperforms a well-known existing EMOA.
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