IJSRD - International Journal for Scientific Research & Development| Vol.

3, Issue 09, 2015 | ISSN (online): 2321-0613

Reliable Data Transmission to Mobile Cloud Computing from Wireless
Sensor Network
Amey Vajpeyi1 Anjali Wagh2 Payal Patil3
1,2,3
Department of Computer Engineering
1,2,3
KVN Naik Institute of Engineering Education and Research, Nashik
Abstract—The combination of ubiquitous wireless sensor
Network (WSN) and powerful mobile cloud computing
M.C.C. is an investigation topic that is fascinating growing
attention in both academic and usefulness. In this new
example, WSN delivers data to the cloud and moveable
users demand data from the cloud. To provision applications
involving W.S.N.-M.C.C. combination, which need to
dependably offer data that are more beneficial to the
moveable users from W.S.N. to cloud, this project first
recognizes the critical problems that disturb the helpfulness
of sensory data and the dependability of W.S.N., then
suggests a novel W.S.N.-M.C.C. integration scheme called
T.P.S.S., which contains of 2 main parts: 1) time & priority
based selective data transmission T.P.S.D.T. for W.S.N.
gateway to selectively transfer sensory data that are more
valuable to the cloud, seeing the time and priority features of
the data demanded by the mobile user and 2)PSS algorithm
which is priority based sleep scheduling algorithm for
W.S.N. to save energy depletion so that it can gather and
transmit data in a More trustworthy way. Diagnostic and
investigational results demonstrate the efficiency of T.P.S.S.
in improving usefulness of sensory data and reliability of
W.S.N. for W.S.N.-M.C.C. integration.
Key words: MCC, WSN
I. INTRODUCTION
A. Integration of WSN and MCC
Wireless sensor network (W.S.N.) is a distributed network,
containing of independent sensors that supportively monitor
the physical or ecological conditions (eg, sound,
temperature, humidity, shaking, etc.). With the pervasive
data gathering ability of sensors, W.S.N. has great
prospective to enable a lot of substantial applications in
numerous areas of industry, resident and military (eg,
industrial process observing, forest fire detection,
battleground surveillance, etc.) which could alter how
people get connected with the physical world. A good
example is forest fire discovery by deploying a large no. of
dispersed sensors into the forest to continuously monitor
high temperature, moisture and gases, forest fire could be
sensed in a well-timed manner and how a fire is 84 probable
to spread out could be strong-minded, without the physical
remark from employees on the ground.
Furthermore, congenital from cloud computing
(C.C.), which is a new computing example enabling users to
elastically consume a shared pool of cloud resources (e.g.,
processors, storages, applications, services) in an on-demand
fashion, mobile C.C. (M.C.C.) further transfers the data
storage and data processing tasks from the mobile devices to
the powerful cloud. Thus, M.C.C. not only eases the
limitations (e.g., battery, processing power, storage
capacity) of mobile devices but also improves the
performance of a lot of outdated mobile services (e.g.,
mobile learning, mobile gaming, and mobile health). For
example, mobile gaming can exploit M.C.C. to change the

game engine that requires considerable computing resources
(eg, graphic rendering) from mobile devices to great servers
in the cloud to significantly reduce the vigor usage of the
mobile devices and increase the gaming performance (e.g.,
sound effect, image definition, refresh rate).

Lately, motivated by the potentials of
supplementing the pervasive data gathering capabilities of
W.S.N. with the influential data storage and data processing
capabilities of M.C.C., the integration of W.S.N. and
M.C.C. is fascinating increasing attention from both
academia and industry. Particularly, as illustrated in Fig. 1
about the general scheme (G.S.) to gather and transmit
sensory data for W.S.N-M.C.C. integration, the sensory data
(eg, traffic, humidity, weather, house monitoring
information) collected by various types of always on sensors
(e.g., mobile sensors, static sensors, video sensors,) after
data processing, data storage and, data sensing are
transferred first to the W.S.N. gateway in a hop-by-hop
manner. The gateway then moreover stores, processes and
transfers the received sensory data to the cloud. Lastly, the
cloud stocks, processes and transfers the sensory data to
mobile user’s on-demand. During the whole data
communication process, if the data communications to the
gateway from the sensor nodes or to the cloud from the
gateway or to the mobile user from the cloud are not
successful, data are retransmitted till they are successfully
transported.
For this new W.S.N.-M.C.C. combination
paradigm, the W.S.N. acts as the data source for the cloud
and mobile users are the data supplicants for the cloud. With
just a modest client on their mobile devices, mobile users
can have access to their required sensory data from the
cloud, wherever and whenever there is network
communication. Evolving as the concept of ``sensor cloud'',
the integrated W.S.N.-M.C.C. is ``an substructure that
allows truly pervasive calculation using sensors as an
interaction between cyber and physical worlds, the data

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Reliable Data Transmission to Mobile Cloud Computing from Wireless Sensor Network
(IJSRD/Vol. 3/Issue 09/2014/027)

compute clusters as the cyber backbone and the internet as
the communication media''
And consequently reports any event to a location
aware cloud service in real time. In case of an interruption
event, another cloud service alerts the user with a S.M.S.
conversation of mobile users, C.L.S.S. dynamically controls
the awake or asleep status of each sensor node to decrease
energy consumption of the combined W.S.N.
II. SYSTEM ARCHITECTURE
A. Overview
Fig 2 shows the proposed T.P.S.S. scheme to collect and
transmit sensory data for W.S.N.-M.C.C. integration,
towards reliably offering data which are more valuable to
the mobile users from W.S.N. to cloud. The thorough steps
of T.P.S.S. for each W.S.N. to gather and transmit sensory
data for each corresponding mobile user are depicted as
follows.

1) Sensor nodes govern their awake and asleep states with
P.S.S.
2) Sensor nodes sense the environmental data with a fixed
frequency and stock the sensory data plus process the
sensory data.
3) Sensor nodes send the managed sensory data to the
gateway ‘g’ with the hop-by-hop many to one and
pattern.
4) Gateway ‘g’ stores the acknowledged sensory data and
then manipulates the sensory data.
5) Gateway g selectively transmits the sensory data to the
cloud C with TPSDT.
6) Cloud C additional stores and processes the received
sensory data.
7) If data transmission from i to g or g to C practices data
losses or failures, i or g performs data retransmission
until the data broadcast is positive.
8) Mobile user u concerns data requests to cloud C and
cloud C transfers the demanded sensory data to the
mobile user ‘u’.
9) If data communication from C to u meets data losses or
failures, C implements data retransmission till the data
communication is successful.
10) Cloud C vigorously updates the P.T.P. table with
equation (1) if the priority and time features of the

demanded data of the mobile user are changed and
sends the updated P.T.P. table to gateway in each time
period t.
B. Scheme Characteristics and Analysis
Relating Fig. 1 and Fig. 2, based on the above introductions,
we can see that our proposed T.P.S.S. shares the similar
technique with G.S. (i.e. data retransmission) to mitigate
data transmission losses or failures in sensory data
transmissions for improving the dependability of W.S.N.
during W.S.N.-M.C.C. combination.
In addition, we can detect that T.P.S.S. varies from
G.S. to collect and transmit sensory data for W.S.N.-M.C.C.
integration, with respect to the following two aspects.
1) TPSDT for WSN Gateway
In our suggested T.P.S.S., the gateway g selectively
transmits the sensory data to the cloud C with T.P.S.D.T.
This design is to enhance the effectiveness of sensory data,
since TPSDT data transmission is based on the P.T.P. table
inferred from the time and priority features of the data
requested by the mobile user. Thus, generally the
successfully transmitted sensory data to the cloud will all be
utilized to answer mobile user data requests. In the case that
the mobile user u concerns data requests for sensory data
currently not stored in the cloud C in the time period t , as
the P.T.P. table is dynamically updated with equation (1) if
the time and priority features of the demanded data of the
mobile user are altered in t (Step 10 of the T.P.S.S.), running
the P.S.S. algorithm with the upgraded P.T.P. table in t
makes the sensor nodes from which mobile user requires
data awake (property 1 of PSS).That means, the cloud is
capable of replying the unexpected data requests by
dynamically updating PTP table. Furthermore, as the
sensory data are selectively transmitted from W.S.N.
gateway to the cloud with T.P.S.D.T., the bandwidth
requisite and network bottleneck are reduced meanwhile.
This can also improve the reliability of W.S.N., as it eases
the data communication loss failure or loss problem to some
extent.
2) P.S.S. for W.S.N.
In our proposed T.P.S.S., the sensor nodes dynamically
conclude their awake and asleep states according to P.S.S
This design can greatly improve the reliability of W.S.N.,
Subsequently P.S.S. can greatly save the energy ingesting
and prolong the network lifetime (property 3 of PSS) so that
W.S.N. can collect and transmit data longer. Specially, when
sensors normally work for a certain period of time, the
energy of the sensors will be consumed quickly and sensor
nodes will die and cannot work anymore. With P.S.S.,
sensor nodes are vigorously asleep and awake and only a
subset of sensor nodes with more remaining energy are
obligatory to be awake in each time period, this will greatly
improve the sensory energy reduction issue that seriously
shakes the reliability of W.S.N. and the life of W.S.N. will
be greatly improved.
III. RELATED WORK
There are a several workings associated to W.S.N.-M.C.C.
integration. They mainly focus on the following two aspects:
 Improvising the performance of W.S.N., and
 Making Better use of the data collected by the
W.S.N.

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Reliable Data Transmission to Mobile Cloud Computing from Wireless Sensor Network
(IJSRD/Vol. 3/Issue 09/2014/027)

Specifically, with respect to:
Improving the performance of W.S.N. with
W.S.N.-M.C.C. combination, it is claimed in that the
integration of W.S.N. and M.C.C. is able to provision the
dynamic loads that are produced by environmental W.S.N.
applications. It is illustrated in that the combination of
W.S.N. and M.C.C. could address the challenge of data
organization in W.S.N. for patient supervision. Moreover, it
is recommended in that the cloud could possibly improve
the visualization performance of a W.S.N. for living
environments. A cooperative location based sleep
scheduling procedure is proposed in to increase the network
lifetime performance of the combined W.S.N. This
algorithm addresses the WSN dependability issue to some
extent by increasing the network life. Nevertheless, the
helpfulness of sensory data is not considered.
IV. CONCLUSION AND FUTURE WORK
In this project, we have concentrated on W.S.N.-M.C.C.
combination by incorporating the pervasive data gathering
ability of W.S.N. and the influential data storage and data
processing abilities of M.C.C Mainly, to support W.S.N.M.C.C. combination presentations that need more beneficial
data offered dependably from the W.S.N. to the cloud, we
have recognized the critical issues that obstruct the
usefulness of sensory data and dependability of W.S.N., and
offered a novel W.S.N.-M.C.C. integration system named
T.P.S.S. to address some of these problems. Precisely,
T.P.S.S. consists of the subsequent two main parts:
 T.P.S.D.T. for W.S.N. gateway to selectively transmit
sensory data that are more beneficial to the cloud,
taking in view the time and priority features of the data
demanded by the mobile user
 P.S.S. procedure for W.S.N. to save energy depletion
so that it can gather and transmit data more reliably.
Both investigative and experimental results regarding
T.P.S.S. have been presented to demonstrate the
effectiveness of T.P.S.S. in improving the usefulness
of sensory data and dependability of W.S.N. for
W.S.N.-M.C.C. integration.

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