Iaetsd an enhancement for content sharing over

An Enhancement for Content Sharing Over
Smartphone-Based Delay Tolerant Networks
P. Guru Tejaswini, K. Phalguna Rao
Student, Audisankara institute of technology,
Gudur, Nellore, Andhra Pradesh, India.
tejaswini.guru582@gmail.com
HOD, Audisankara institute of technology,
Gudur, Nellore, Andhra Pradesh, India.
Abstract: From the last few years, the Smartphone users has swiftly increased so peer-to-peer ad hoc content sharing is liable to crop up frequently.
As the usual data delivery schemes are not proficient for content sharing because of random connectivity amid Smartphone’s latest content sharing
mechanisms should be developed. To achieve data delivery in such exigent environments, researchers have anticipated the use of encounter-based
routing or store-carry-forward protocols, in this a node stores a message may be a note and carries it until a forwarding chance arises through an
encounter with other node. Earlier studies in this field focused on whether two nodes would come across each other and the place and time of
encounter. This paper proposes discover-predict-deliver as proficient content sharing scheme.Here we make use of a hidden markov model in order to
predict the future mobility information of individual's .The existing system approximately consequences in a 2 percent CPU overhead , diminish the
Smartphone battery lifetime by 15 percent .So to minimize energy consumption we propose the use of sensor scheduling schemes in an opportunistic
context.
Key words: Encounter Based Routing, Content Sharing, sensor scheduling Schemes, hidden markov model
1.Introduction
The Smartphone users have been rapidly increasing
day-by day[1]. A Smartphone consists of more advanced computing
capability and connectivity than basic phones. As interfaces of
Smartphone are more handy and accessible users can share any type
of contents like images, videos such multimedia content. But content
sharing is bothersome. It involves numerous user activities. To
minimize users burden we can depend upon an ad hoc technique of
peer-to-peer content sharing. Mobile ad hoc network is characterized
as multi-hop wireless communications between mobile device.
Smartphone's consists of many network interfaces like Bluetooth and
Wi-Fi so ad hoc networks can be easily constructed with them. The
Connectivity among Smartphone's is likely to be alternating because
of movement patterns of carriers and the signal transmission
phenomena. A wide variety of Store-carry-forward protocols have
been anticipated by researchers.
Routing in delay-tolerant networking concerns itself
with the ability to route, data from a source to a destination, which is
a vital ability of all communication networks must have. In these
exigent environments, mostly used or familiar ad hoc routing
protocols fail to launch routes. This is because , these protocols first
try to establish a complete route and then, once the route has been
established forwards the actual data. Still, when immediate end-to-
end paths are complicated or unfeasible to institute, routing protocols
should take to a "store and then forward" method or approach, where
data or a message is moved and stored incrementally all over the
network in hops that it will finally arrive at its destination. A general
technique used to maximize the likelihood of a message being
effectively transferred is to duplicate many copies of the message in
hops that one will be successful in reaching its destination.
Delay Tolerant Network (DTN) routing protocols attain
enhanced performance than usual ad hoc routing protocols. Over
the anticipated DTN routing protocols, Epidemic routing is an
vital DTN routing solution. In Epidemic routing by vahdat et al[2],
messages are forwarded to each encountered node that does not have
a replica of the same message. This solution exhibits the finest
performance in terms of delivery pace and latency, but it involves
abundant resources, such as storage, bandwidth, and energy.
This paper spotlight mainly on efficiency of content
discovery and its delivery to the targeted destination. Here we
suggest recommendation based discover-predict-deliver(DPD) as
efficient and effective content sharing scheme for smart phone
based DTN’s. DPD suppose that smart phones can hook up when
they are in close proximity that is where the Smartphone users reside
for a longer period. Earlier studies have shown that Smartphone
users stay indoors for a longer period where GPS cannot be
accessed.
The objective of our work is to discover solutions to the
problems in content sharing and to minimize the energy
consumption using sensor scheduling schemes.
2. Related work
A delay tolerant network (DTN) is a mobile network
where a existing source-destination path may not exist amid a pair of
nodes and messages are forwarded in a store-carry-forward routing
hypothesis [6].
The objective of our work is to discover the
content sharing problem in Smartphone based DTN’s involves
minimizing energy consumption using sensor scheduling schemes.
Content sharing in DTN'S involves the following problems:
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2.1. Content sharing
In this segment we examine the problem of
content sharing in delay tolerant networks and depict substitute
solutions. As specified in the introduction, we spotlight on mobile
opportunistic networking scenarios where the nodes will be
communicating using the DTN bundle protocol. A few devices in the
network store content which they are ready to share with others. All
nodes are willing to assist and provide a restricted amount of their
local system resources (bandwidth, storage, and dispensation power)
to aid other nodes. Our objective is to permit users to issue queries
for content that is stored on the other nodes everywhere in the
network and consider the possibility of such a node to acquire the
required information. To ease searching, we suppose that nodes are
capable to carry out searches on their local storage and uncover the
appropriate results for a given query. The content sharing process is
characterized into two stages: the content discovery phase and the
content delivery phase. In content discovery phase, the user inputs
or enters in a content sharing application requests for the content.
The application initially searches the content it its own or individual
database and if not found, the application then creates a query that is
forwarded based on the user’s request. The content delivery phase is
commenced, only when the content is found and the content is then
forwarded to the query originator.
Figure: Processing of incoming query.
2.1.1 Content discovery
In content discovery, mainly systems spotlight on how to
formulate queries, that depends on assumptions about the format or
layout of the content to be discovered. A common protocol should
sustain various forms of queries and content, but we summarize from
the actual similar or matching process in order to spotlight on
discovering content in the network. The easiest strategy to discover
and deliver the contents is Epidemic routing. But, due to resource
limits, Epidemic routing is regularly extravagant, so we have to
consider methods that limits the system resources used up on both
content discovery and delivery. Preferably, a query should only be
forwarded to neighbours that hold on the matching contents or those
are on the pathway to other nodes having matching content .
Different nodes should return no overlapping responses to the
requester. As total knowledge or active coordination is not an
alternative in our state, one node can merely make autonomous
forwarding decisions. These autonomous forwarding decisions
should attain a fine trade off amid discovery efficiency and
necessary resources. Analogous limitations pertain to content
delivery. A few methods anticipated by Pitkanen et al. may be used
for restraining the distribution of queries. Additionally, we study two
substitutes for restraining the distribution of queries:a query distance
limit and split query lifetime limit. We employ the controlled
replication-based [9] routing scheme that performs a single-copy
scheme. This single-copy scheme turn both query lifetime and
distance limits into random walk, and the scheme is not powerful as
soon as content-carrier nodes (i.e., destinations) are not eminent. By
distinguishing, the controlled replication-based scheme dispenses a
set of message replicas and evade the excessive spread of messages.
2.1.2. Content delivery
When the query matching content is discovered, the
content carrying node should transmit only a subset of results. This
constraint is needed to limit the amount of resources utilized both
locally and globally for sending and storing the responses, and to
eliminate potential copies . The query originator sets a limit for both
the number of replications or duplicates and the amount of content
that should be produced. When nodes require to forward a query
message,
the limits incorporated in the query message are used to make the
forwarding decision. If the amount of the content go beyond the
response limit, the node wants to select which ones to forward.
2.2. Mobility Prediction
Numerous studies have largely specified another
problem of content sharing: mobility learning and prediction.
Beacon Print discover meaningful places by constantly determining
constant scans for a time period. Place Sense senses the arrival and
exit from a place by utilizing invasive RF-beacons. The system uses
a radio beacon’s retort rates to attain vigorous beacon conclusion.
EnTracked is a position tracking system for GPS-enabled devices.
The system is configurable to recognize different tradeoffs amid
energy consumption and heftiness.
Mobility prediction has been extensively
studied in and out of the delay-tolerant networking area. Markov-
based schemes, make the problem as a Hidden Markov or semi-
Markov model and probabilistic prediction of human mobility. In
contrast, neural network based schemes try to match the observed
user behaviour with earlier observed behaviour and estimate the
prospect based on the experimental patterns.
Markov based schemes are suitable for resource- restricted devices,
like smartphones, owing to their low computation overhead and
reserved storage requirements. In our work, we have to develop a
mobility learning and prediction method. This method has been
built to offer coarse-grained mobility information with a less
computation overhead. When the difficulty of mobility learning and
prediction scheme can be mistreated, the schemes specified in can
be worn to offer fine-grained mobility information.
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3.Problem Definition
In the existing system, the energy consumption is more so
the battery lifetime will be reduced. By using sensor scheduling
mechanisms energy consumption can be reduced and can increase
the lifespan of the batteries.
4. Proposed System
Figure: Mean energy consumption in a day
Fig shows the daily energy utilization outline, which is calculated
using the composed stationary and movement time from seven
different users in four weeks . The scrutiny does not comprise the
energy utilization of content swap as it mainly rely on the volume
and communication pace of the nodes. The typical energy
consumptions of GPS, Wi-Fi, and the accelerometer varies. The
accelerometer do have the maximum energy consumption as it is
used endlessly over 24 hours. Wi-Fi energy utilization is observed
owed to the scanning of neighbour APs for place recognition. GPS
has a huge discrepancy in energy consumption as this may not be
available in all places.
In order to minimize energy consumption or utilization we use
sensor scheduling schemes or mechanisms[10]. Sensor systems
have an wide-ranging diversity of prospective, functional and
important applications. In any case, there are questions that have to
be inclined for prolific procedure of sensor system frameworks in
right applications. Energy sparing is one fundamental issue for
sensor systems as most of the sensors are furnished with no
rechargeable batteries that have constrained lifetime. To enhance the
lifetime of a sensor set up, one vital methodology is to attentively
schedule sensors' work sleep cycles (or obligation cycles). In
addition, in cluster based systems, grouping heads are usually
selected in a way that minimizes or reduces the aggregate energy
utilization and they may axle among the sensors to fine-tune energy
utilization. As a rule, these energy productive scheduling
components or mechanisms (furthermore called topology
arrangement components) required to accomplish certain application
requirements while sparing energy. In sensor arranges that have
various outline requirements than those in conventional remote
systems. Distinctive instruments may make characteristic suspicions
about their sensors together with identification model, sense zone,
transmission scope, dissatisfaction or disappointment model, time
management, furthermore the capability to get area and parting data.
5.Results
5.1.Learning accuracy
Learning accuracy demonstrates how capably and
exactly the places were identified. The accuracy of place learning
influence the evaluation of encounter opportunity amid two nodes.
For example, if two distinct places are recognized as identical ones,
we may improperly estimate that two nodes will encounter each
other when they visit two distinct places. Also, the accurate
computation of and rely on the geographical location information of
the nodes.
Figure: learning accuracy
5.2.Discovery Efficiency
The ratio of discovered contents to the generated queries within a
specified period or time is discovery ratio or efficiency. DPD’s
discovery performance is skewed to the two forwarding. In Epidemic
routing , queries are forwarded to each and every node. In hops-10
and hops-5, a query message is then forwarded till its hop count
achieve 10 and 5, correspondingly. When a query matching content
is accessible only on a small number of nodes, the discovery
methods illustrates a low discovery speed. With an rising query
lifespan, both DPD and Epidemic demonstrate a high discovery ratio
since with a longer time, each query is forwarded to more number of
nodes.
Figure: Discovery Efficiency
5.3.Prediction accuracy
Mobility prediction is a main aspect in the estimation of utility
function. Here, we estimate our prediction process according to
trajectory deviation, prediction accuracy, as shown in the figure
below. Trajectory deviation specify the abnormality of a user’s
mobility. For this assessment, we mutate the existing mobility
information with noise data. Thus, 10, 20, and 30 % of the
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meaningful places are chosen at random locations for trajectory
deviations of 0.1, 0.2, and 0.3, correspondingly. So as the trajectory
deviation raise, the prediction accuracy drop off. Prediction accuracy
is calculated as the ratio of accurately predicted locations to the
overall predicted locations.
Figure: Prediction accuracy
6.Conclusion
In this paper we have proposed a proficient content
sharing scheme for Smartphone based DTN’s. In this we have
anticipated discover-predict-deliver as a effectual content sharing
method which is capable of discovering the content and delivers it
to the appropriate destination. The scheme also present the mobility
information of individuals. We made an try to make use of the
availability and communication technology of current Smartphone.
We have also compared our proposed scheme with traditional
schemes.
In this paper we have also proposed sensor scheduling
schemes to enhance the lifespan of a battery. By the effectiveness of
the sensing in sensor scheduling we can reduce energy consumption
of the smartphones.
Finally, our system has still has room for improvement
by considering the privacy issues.
6. References
[1] T3IGroupLLc , Http:://www.telecomweb.com, 2010.
[2] A. Vahdat and D. Becker, “Epidemic Routing for Partially
Connected Ad-Hoc Networks,” technical reports, Dept. of Computer
Science and engineering, Duke Univ.., Sept. 2000.
[3] A. Balasubramanian, B.N. Levine, and A. Venkataramani.,
“DTN Routing as a .Resource Allocation Problem,” Proc. ACM
SIGCOMM.., pp. 373-384, 2007.
[4] R.C. Shah, S. Roy, S. Jain, and W. Brunette, “Data Mules-
Modeling a Three-Tier Architecture for Sparse Sensor Networks,”
Elsevier Ad Hoc Networks J...., vol. 1, pp. 215-233, Sept. 2003.
[5] A. Lindgren, A. Doria, and O. Schelen, “Probabilistic -Routing in
Intermittently Connected Networks,” SIGMOBILE Mobile
Computer Comm....Rev., vol. 7, no. 3, pp. 19-20, 2003.
[6] C. Liu and J. Wu, “An Optimal Probabilistic Forwarding
Protocol in Delay Tolerant Networks,” Proc. ACM MobiHoc, pp. 14,
2009.
[7] J. Wu, M. Lu, and F. Li, “Utility-Based Opportunistic Routing in
MultiHop Wireless Networks,” Proc. *28th Int’l Conf. Distributed
Computing Systems (ICDCS ’08), pp. 470-477,+ 2008.
[8] T. Spyropoulos, K. Psounis, and C.S. Raghavendra, “Spray and
Wait: An Efficient Routing Scheme for Intermittently Connected -
Mobile Networks,” Proc. ACM SIGCOMM Workshop Delay-
Tolerant Networking (WDTN ’05), pp.. 252-259, 2005.
[9] T. Spyropoulos, K. Psounis, and C. Raghavendra, *“Efficient
Routing in Intermittently Connected Mobile Networks: The Single
Copy Case,” IEEE/ACM Trans. Networking,/. vol. 16, no. 1, pp. 63-
76, Feb. 2008.
[10] Ling Shi, Michael Epstein, Bruno Sinopoli and
Richard.M.Murray," Effective Sensor Scheduling Schemes
Employing Feedback in the
Communication Loop"
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