Java Titles & abstracts 2013-2014

IEEE- Project Title 2013
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33,Meenakshi Sundaram Building, Sivaji st, (Near Tnagar Bus Terminus),TNagar Chennai-17, Ph: 044-42070551
Mobile: 9025439777/ www.sybiantechnologies.com / sybianprojects@gmail.com
JAVA
BIO MEDICAL / MEDICAL IMAGING
1. Simultaneously Identifying All True Vessels From Segmented Retinal
Images
ABSTRACT:
Measurements of retinal blood vessel morphology have been shown to be
related to the risk of cardiovascular diseases. The wrong identification of vessels
may result in a large variation of these measurements, leading to a wrong clinical
diagnosis. In this paper, we address the problem of automatically identifying true
vessels as a postprocessing step to vascular structure segmentation. We model the
segmented vascular structure as a vessel segment graph and formulate the problem
of identifying vessels as one of finding the optimal forest in the graph given a set
of constraints. We design a method to solve this optimization problem and evaluate
it on a large real-world dataset of 2446 retinal images. Experiment results are
analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and
98.7% recall of the true vessels for clean segmented retinal images, and remains
robust even when the segmented image is noisy.

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2. A modified fuzzy c-means algorithm for bias field estimation and
segmentation of MRI data
ABSTRACT:
We present a novel algorithm for fuzzy segmentation of magnetic resonance
imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic.
MRI intensity inhomogeneities can be attributed to imperfections in the radio-
frequency coils or to problems associated with the acquisition sequences. The
result is a slowly varying shading artifact over the image that can produce errors
with conventional intensity-based classification. Our algorithm is formulated by
modifying the objective function of the standard fuzzy c-means (FCM) algorithm
to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel)
to be influenced by the labels in its immediate neighborhood. The neighborhood
effect acts as a regularizer and biases the solution toward piecewise-homogeneous
labelings. Such a regularization is useful in segmenting scans corrupted by salt and
pepper noise. Experimental results on both synthetic images and MR data are given
to demonstrate the effectiveness and efficiency of the proposed algorithm.
3. Feature-Based Image Patch Approximation for Lung Tissue
Classification
ABSTRACT:
In this paper, we propose a new classification method for five categories of
lung tissues in high-resolution computed tomography (HRCT) images, with
feature-based image patch approximation. We design two new feature descriptors

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for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary
patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented
gradients (MCHOG) gradient descriptor. Together with intensity features, each
image patch is then labeled based on its feature approximation from reference
image patches. And a new patch-adaptive sparse approximation (PASA) method is
designed with the following main components: minimum discrepancy criteria for
sparse-based classification, patch-specific adaptation for discriminative
approximation, and feature-space weighting for distance computation. The patch-
wise labelings are then accumulated as probabilistic estimations for region-level
classification. The proposed method is evaluated on a publicly available ILD
database, showing encouraging performance improvements over the state-of-the-
arts.
4. Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans
Based on Fissures, Vessels, and Bronchi
ABSTRACT:
Segmentation of the pulmonary lobes is relevant in clinical practice and
particularly challenging for cases with severe diseases or incomplete fissures. In
this work, an automated segmentation approach is presented that performs a
marker-based watershed transformation on computed tomography (CT) scans to
subdivide the lungs into lobes. A cost image for the watershed transformation is
computed by combining information from fissures, bronchi, and pulmonary
vessels. The lobar markers are calculated by an analysis of the automatically
labeled bronchial tree. By integration of information from several anatomical

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structures the segmentation is made robust against incomplete fissures. For
evaluation the method was compared to a recently published method on 20 CT
scans with no or mild disease. The average distances to the reference segmentation
were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor
fissure, respectively. In addition the results were submitted to LOLA11, an
international lung lobe segmentation challenge with publically available data
including cases with severe diseases. The average distances to the reference for the
55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major,
right major, and right minor fissure. Moreover, an analysis of the relation between
segmentation quality and fissure completeness showed that the method is robust
against incomplete fissures.

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CLOUD COMPUTING
1. Efficient Resource Mapping Framework over Networked Clouds via
Iterated Local Search-Based Request Partitioning
ABSTRACT:
The cloud represents a computing paradigm where shared configurable
resources are provided as a service over the Internet. Adding intra- or intercloud
communication resources to the resource mix leads to a networked cloud
computing environment. Following the cloud infrastructure as a Service paradigm
and in order to create a flexible management framework, it is of paramount
importance to address efficiently the resource mapping problem within this
context. To deal with the inherent complexity and scalability issue of the resource
mapping problem across different administrative domains, in this paper a
hierarchical framework is described. First, a novel request partitioning approach
based on Iterated Local Search is introduced that facilitates the cost-efficient and
online splitting of user requests among eligible cloud service providers (CPs)
within a networked cloud environment. Following and capitalizing on the outcome
of the request partitioning phase, the embedding phase-where the actual mapping
of requested virtual to physical resources is performed can be realized through the
use of a distributed intracloud resource mapping approach that allows for efficient
and balanced allocation of cloud resources. Finally, a thorough evaluation of the
proposed overall framework on a simulated networked cloud environment is
provided and critically compared against an exact request partitioning solution as
well as another common intradomain virtual resource embedding solution.

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2. Hessian Regularized Support Vector Machines for Mobile Image
Annotation on the Cloud
ABSTRACT:
With the rapid development of the cloud computing and mobile service,
users expect a better experience through multimedia computing, such as automatic
or semi-automatic personal image and video organization and intelligent user
interface. These functions heavily depend on the success of image understanding,
and thus large-scale image annotation has received intensive attention in recent
years. The collaboration between mobile and cloud opens a new avenue for image
annotation, because the heavy computation can be transferred to the cloud for
immediately responding user actions. In this paper, we present a scheme for image
annotation on the cloud, which transmits mobile images compressed by Hamming
compressed sensing to the cloud and conducts semantic annotation through a novel
Hessian regularized support vector machine on the cloud. We carefully explained
the rationality of Hessian regularization for encoding the local geometry of the
compact support of the marginal distribution and proved that Hessian regularized
support vector machine in the reproducing kernel Hilbert space is equivalent to
conduct Hessian regularized support vector machine in the space spanned by the
principal components of the kernel principal component analysis. We conducted
experiments on the PASCAL VOC'07 dataset and demonstrated the effectiveness
of Hessian regularized support vector machine for large-scale image annotation.

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3. An Adaptive Cloud Downloading Service
ABSTRACT:
Video content downloading using the P2P approach is scalable, but does not
always give good performance. Recently, subscription-based premium services
have emerged, referred to as cloud downloading. In this service, the cloud storage
and server caches user-interested content and updates the cache based on user
downloading requests. If a requested video is not in the cache, the request is held in
a waiting state until the cache is updated. We call this design server mode. An
alternative design is to let the cloud server serve all downloading requests as soon
as they arrive, behaving as a helper peer. We call this design helper mode. Our
model and analysis show that both these designs are useful for certain operating
regimes. The helper mode is good at handling a high request rate, while the server
mode is good at scaling with video population size. We design an adaptive
algorithm (AMS) to select the service mode automatically. Intuitively, AMS
switches service mode from server mode to helper mode when too many peers
request blocked movies, and vice versa. The ability of AMS to achieve good
performance in different operating regimes is validated by simulation .
4. A Highly Practical Approach toward Achieving Minimum Data Sets
Storage Cost in the Cloud
ABSTRACT:
Massive computation power and storage capacity of cloud computing
systems allow scientists to deploy computation and data intensive applications

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without infrastructure investment, where large application data sets can be stored
in the cloud. Based on the pay-as-you-go model, storage strategies and
benchmarking approaches have been developed for cost-effectively storing large
volume of generated application data sets in the cloud. However, they are either
insufficiently cost-effective for the storage or impractical to be used at runtime. In
this paper, toward achieving the minimum cost benchmark, we propose a novel
highly cost-effective and practical storage strategy that can automatically decide
whether a generated data set should be stored or not at runtime in the cloud. The
main focus of this strategy is the local-optimization for the tradeoff between
computation and storage, while secondarily also taking users' (optional)
preferences on storage into consideration. Both theoretical analysis and simulations
conducted on general (random) data sets as well as specific real world applications
with Amazon's cost model show that the cost-effectiveness of our strategy is close
to or even the same as the minimum cost benchmark, and the efficiency is very
high for practical runtime utilization in the cloud.
5. AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and
Efficient Social Video Sharing in the Clouds
ABSTRACT:
While demands on video traffic over mobile networks have been souring,
the wireless link capacity cannot keep up with the traffic demand. The gap between
the traffic demand and the link capacity, along with time-varying link conditions,
results in poor service quality of video streaming over mobile networks such as
long buffering time and intermittent disruptions. Leveraging the cloud computing

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technology, we propose a new mobile video streaming framework, dubbed AMES-
Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and
efficient social video sharing (ESoV). AMoV and ESoV construct a private agent
to provide video streaming services efficiently for each mobile user. For a given
user, AMoV lets her private agent adaptively adjust her streaming flow with a
scalable video coding technique based on the feedback of link quality. Likewise,
ESoV monitors the social network interactions among mobile users, and their
private agents try to prefetch video content in advance. We implement a prototype
of the AMES-Cloud framework to demonstrate its performance. It is shown that
the private agents in the clouds can effectively provide the adaptive streaming, and
perform video sharing (i.e., prefetching) based on the social network analysis.
6. Efficient Resource Provisioning and Rate Selection for Stream Mining
in a Community Cloud
ABSTRACT:
Real-time stream mining such as surveillance and personal health
monitoring, which involves sophisticated mathematical operations, is computation-
intensive and prohibitive for mobile devices due to the hardware/computation
constraints. To satisfy the growing demand for stream mining in mobile networks,
we propose to employ a cloud-based stream mining system in which the mobile
devices send via wireless links unclassified media streams to the cloud for
classification. We aim at minimizing the classification-energy cost, defined as an
affine combination of classification cost and energy consumption at the cloud,
subject to an average stream mining delay constraint (which is important in real-

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time applications). To address the challenge of time-varying wireless channel
conditions without a priori information about the channel statistics, we develop an
online algorithm in which the cloud operator can dynamically adjust its resource
provisioning on the fly and the mobile devices can adapt their transmission rates to
the instantaneous channel conditions. It is proved that, at the expense of increasing
the average stream mining delay, the online algorithm achieves a classification-
energy cost that can be pushed arbitrarily close to the minimum cost achieved by
the optimal offline algorithm. Extensive simulations are conducted to validate the
analysis.
7. ptimal Multiserver Configuration for Profit Maximization in Cloud
Computing
ABSTRACT:
As cloud computing becomes more and more popular, understanding the
economics of cloud computing becomes critically important. To maximize the
profit, a service provider should understand both service charges and business
costs, and how they are determined by the characteristics of the applications and
the configuration of a multiserver system. The problem of optimal multiserver
configuration for profit maximization in a cloud computing environment is studied.
Our pricing model takes such factors into considerations as the amount of a
service, the workload of an application environment, the configuration of a
multiserver system, the service-level agreement, the satisfaction of a consumer, the
quality of a service, the penalty of a low-quality service, the cost of renting, the
cost of energy consumption, and a service provider's margin and profit. Our

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approach is to treat a multiserver system as an M/M/m queuing model, such that
our optimization problem can be formulated and solved analytically. Two server
speed and power consumption models are considered, namely, the idle-speed
model and the constant-speed model. The probability density function of the
waiting time of a newly arrived service request is derived. The expected service
charge to a service request is calculated. The expected net business gain in one unit
of time is obtained. Numerical calculations of the optimal server size and the
optimal server speed are demonstrated.
8. CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring
ABSTRACT:
Cloud-assisted mobile health (mHealth) monitoring, which applies the
prevailing mobile communications and cloud computing technologies to provide
feedback decision support, has been considered as a revolutionary approach to
improving the quality of healthcare service while lowering the healthcare cost.
Unfortunately, it also poses a serious risk on both clients' privacy and intellectual
property of monitoring service providers, which could deter the wide adoption of
mHealth technology. This paper is to address this important problem and design a
cloud-assisted privacy preserving mobile health monitoring system to protect the
privacy of the involved parties and their data. Moreover, the outsourcing
decryption technique and a newly proposed key private proxy reencryption are
adapted to shift the computational complexity of the involved parties to the cloud
without compromising clients' privacy and service providers' intellectual property.

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Finally, our security and performance analysis demonstrates the effectiveness of
our proposed design.
9. Cloud-Based Image Coding for Mobile Devices—Toward Thousands to
One Compression
ABSTRACT:
Current image coding schemes make it hard to utilize external images for
compression even if highly correlated images can be found in the cloud. To solve
this problem, we propose a method of cloud-based image coding that is different
from current image coding even on the ground. It no longer compresses images
pixel by pixel and instead tries to describe images and reconstruct them from a
large-scale image database via the descriptions. First, we describe an input image
based on its down-sampled version and local feature descriptors. The descriptors
are used to retrieve highly correlated images in the cloud and identify
corresponding patches. The down-sampled image serves as a target to stitch
retrieved image patches together. Second, the down-sampled image is compressed
using current image coding. The feature vectors of local descriptors are predicted
by the corresponding vectors extracted in the decoded down-sampled image. The
predicted residual vectors are compressed by transform, quantization, and entropy
coding. The experimental results show that the visual quality of reconstructed
images is significantly better than that of intra-frame coding in HEVC and JPEG at
thousands to one compression .

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10.Optimizing Cloud Resources for Delivering IPTV Services Through
Virtualization
ABSTRACT:
Virtualized cloud-based services can take advantage of statistical
multiplexing across applications to yield significant cost savings. However,
achieving similar savings with real-time services can be a challenge. In this paper,
we seek to lower a provider's costs for real-time IPTV services through a
virtualized IPTV architecture and through intelligent time-shifting of selected
services. Using Live TV and Video-on-Demand (VoD) as examples, we show that
we can take advantage of the different deadlines associated with each service to
effectively multiplex these services. We provide a generalized framework for
computing the amount of resources needed to support multiple services, without
missing the deadline for any service. We construct the problem as an optimization
formulation that uses a generic cost function. We consider multiple forms for the
cost function (e.g., maximum, convex and concave functions) reflecting the cost of
providing the service. The solution to this formulation gives the number of servers
needed at different time instants to support these services. We implement a simple
mechanism for time-shifting scheduled jobs in a simulator and study the reduction
in server load using real traces from an operational IPTV network. Our results
show that we are able to reduce the load by ~24%(compared to a possible ~31.3%
as predicted by the optimization framework).

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11.A Network and Device Aware QoS Approach for Cloud-Based Mobile
Streaming
ABSTRACT:
Cloud multimedia services provide an efficient, flexible, and scalable data
processing method and offer a solution for the user demands of high quality and
diversified multimedia. As intelligent mobile phones and wireless networks
become more and more popular, network services for users are no longer limited to
the home. Multimedia information can be obtained easily using mobile devices,
allowing users to enjoy ubiquitous network services. Considering the limited
bandwidth available for mobile streaming and different device requirements, this
study presented a network and device-aware Quality of Service (QoS) approach
that provides multimedia data suitable for a terminal unit environment via
interactive mobile streaming services, further considering the overall network
environment and adjusting the interactive transmission frequency and the dynamic
multimedia transcoding, to avoid the waste of bandwidth and terminal power.
Finally, this study realized a prototype of this architecture to validate the feasibility
of the proposed method. According to the experiment, this method could provide
efficient self-adaptive multimedia streaming services for varying bandwidth
environments.

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12.Anchor: A Versatile and Efficient Framework for Resource
Management in the Cloud
ABSTRACT:
We present Anchor, a general resource management architecture that uses
the stable matching framework to decouple policies from mechanisms when
mapping virtual machines to physical servers. In Anchor, clients and operators are
able to express a variety of distinct resource management policies as they deem fit,
and these policies are captured as preferences in the stable matching framework.
The highlight of Anchor is a new many-to-one stable matching theory that
efficiently matches VMs with heterogeneous resource needs to servers, using both
offline and online algorithms. Our theoretical analyses show the convergence and
optimality of the algorithm. Our experiments with a prototype implementation on a
20-node server cluster, as well as large-scale simulations based on real-world
workload traces, demonstrate that the architecture is able to realize a diverse set of
policy objectives with good performance and practicality.
13.Load Rebalancing for Distributed File Systems in Clouds
ABSTRACT:
Distributed file systems are key building blocks for cloud computing
applications based on the MapReduce programming paradigm. In such file
systems, nodes simultaneously serve computing and storage functions; a file is
partitioned into a number of chunks allocated in distinct nodes so that MapReduce
tasks can be performed in parallel over the nodes. However, in a cloud computing

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environment, failure is the norm, and nodes may be upgraded, replaced, and added
in the system. Files can also be dynamically created, deleted, and appended. This
results in load imbalance in a distributed file system; that is, the file chunks are not
distributed as uniformly as possible among the nodes. Emerging distributed file
systems in production systems strongly depend on a central node for chunk
reallocation. This dependence is clearly inadequate in a large-scale, failure-prone
environment because the central load balancer is put under considerable workload
that is linearly scaled with the system size, and may thus become the performance
bottleneck and the single point of failure. In this paper, a fully distributed load
rebalancing algorithm is presented to cope with the load imbalance problem. Our
algorithm is compared against a centralized approach in a production system and a
competing distributed solution presented in the literature. The simulation results
indicate that our proposal is comparable with the existing centralized approach and
considerably outperforms the prior distributed algorithm in terms of load
imbalance factor, movement cost, and algorithmic overhead. The performance of
our proposal implemented in the Hadoop distributed file system is further
investigated in a cluster environment.
14.Towards Trustworthy Resource Scheduling in Clouds
ABSTRACT:
Managing the allocation of cloud virtual machines at physical resources is a
key requirement for the success of clouds. Current implementations of cloud
schedulers do not consider the entire cloud infrastructure neither do they consider
the overall user and infrastructure properties. This results in major security,

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privacy, and resilience concerns. In this paper, we propose a novel cloud scheduler
which considers both user requirements and infrastructure properties. We focus on
assuring users that their virtual resources are hosted using physical resources that
match their requirements without getting users involved with understanding the
details of the cloud infrastructure. As a proof-of-concept, we present our prototype
which is built on OpenStack. The provided prototype implements the proposed
cloud scheduler. It also provides an implementation of our previous work on cloud
trust management which provides the scheduler with input about the trust status of
the cloud infrastructure.

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DATA MINING
1. Anonymization of Centralized and Distributed Social Networks by
Sequential Clustering
ABSTRACT:
We study the problem of privacy-preservation in social networks. We
consider the distributed setting in which the network data is split between several
data holders. The goal is to arrive at an anonymized view of the unified network
without revealing to any of the data holders information about links between nodes
that are controlled by other data holders. To that end, we start with the centralized
setting and offer two variants of an anonymization algorithm which is based on
sequential clustering (Sq). Our algorithms significantly outperform the SaNGreeA
algorithm due to Campan and Truta which is the leading algorithm for achieving
anonymity in networks by means of clustering. We then devise secure distributed
versions of our algorithms. To the best of our knowledge, this is the first study of
privacy preservation in distributed social networks. We conclude by outlining
future research proposals in that direction.
2. A Proxy-Based Approach to Continuous Location-Based Spatial
Queries in Mobile Environments
ABSTRACT:
Caching valid regions of spatial queries at mobile clients is effective in
reducing the number of queries submitted by mobile clients and query load on the

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server. However, mobile clients suffer from longer waiting time for the server to
compute valid regions. We propose in this paper a proxy-based approach to
continuous nearest-neighbor (NN) and window queries. The proxy creates
estimated valid regions (EVRs) for mobile clients by exploiting spatial and
temporal locality of spatial queries. For NN queries, we devise two new algorithms
to accelerate EVR growth, leading the proxy to build effective EVRs even when
the cache size is small. On the other hand, we propose to represent the EVRs of
window queries in the form of vectors, called estimated window vectors (EWVs),
to achieve larger estimated valid regions. This novel representation and the
associated creation algorithm result in more effective EVRs of window queries. In
addition, due to the distinct characteristics, we use separate index structures,
namely EVR-tree and grid index, for NN queries and window queries,
respectively. To further increase efficiency, we develop algorithms to exploit the
results of NN queries to aid grid index growth, benefiting EWV creation of
window queries. Similarly, the grid index is utilized to support NN query
answering and EVR updating. We conduct several experiments for performance
evaluation. The experimental results show that the proposed approach significantly
outperforms the existing proxy-based approaches.
3. Efficient Algorithms for Mining High Utility Itemsets from
Transactional Databases
ABSTRACT:
Mining high utility itemsets from a transactional database refers to the
discovery of itemsets with high utility like profits. Although a number of relevant

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algorithms have been proposed in recent years, they incur the problem of
producing a large number of candidate itemsets for high utility itemsets. Such a
large number of candidate itemsets degrades the mining performance in terms of
execution time and space requirement. The situation may become worse when the
database contains lots of long transactions or long high utility itemsets. In this
paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and
UP-Growth+, for mining high utility itemsets with a set of effective strategies for
pruning candidate itemsets. The information of high utility itemsets is maintained
in a tree-based data structure named utility pattern tree (UP-Tree) such that
candidate itemsets can be generated efficiently with only two scans of database.
The performance of UP-Growth and UP-Growth+ is compared with the state-of-
the-art algorithms on many types of both real and synthetic data sets. Experimental
results show that the proposed algorithms, especially UP-Growth+, not only reduce
the number of candidates effectively but also outperform other algorithms
substantially in terms of runtime, especially when databases contain lots of long
transactions.
4. Multiparty Access Control for Online Social Networks: Model and
Mechanisms
ABSTRACT:
Online social networks (OSNs) have experienced tremendous growth in
recent years and become a de facto portal for hundreds of millions of Internet
users. These OSNs offer attractive means for digital social interactions and
information sharing, but also raise a number of security and privacy issues. While

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OSNs allow users to restrict access to shared data, they currently do not provide
any mechanism to enforce privacy concerns over data associated with multiple
users. To this end, we propose an approach to enable the protection of shared data
associated with multiple users in OSNs. We formulate an access control model to
capture the essence of multiparty authorization requirements, along with a
multiparty policy specification scheme and a policy enforcement mechanism.
Besides, we present a logical representation of our access control model that allows
us to leverage the features of existing logic solvers to perform various analysis
tasks on our model. We also discuss a proof-of-concept prototype of our approach
as part of an application in Facebook and provide usability study and system
evaluation of our method.
5. Discovering Characterizations of the Behavior of Anomalous
Subpopulations
ABSTRACT:
We consider the problem of discovering attributes, or properties, accounting
for the a priori stated abnormality of a group of anomalous individuals (the
outliers) with respect to an overall given population (the inliers). To this aim, we
introduce the notion of exceptional property and define the concept of
exceptionality score, which measures the significance of a property. In particular,
in order to single out exceptional properties, we resort to a form of minimum
distance estimation for evaluating the badness of fit of the values assumed by the
outliers compared to the probability distribution associated with the values
assumed by the inliers. Suitable exceptionality scores are introduced for both

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numeric and categorical attributes. These scores are, both from the analytical and
the empirical point of view, designed to be effective for small samples, as it is the
case for outliers. We present an algorithm, called EXPREX, for efficiently
discovering exceptional properties. The algorithm is able to reduce the needed
computational effort by not exploring many irrelevant numerical intervals and by
exploiting suitable pruning rules. The experimental results confirm that our
technique is able to provide knowledge characterizing outliers in a natural manner.
6. On Identifying Critical Nuggets of Information during Classification
Tasks
ABSTRACT:
In large databases, there may exist critical nuggets-small collections of
records or instances that contain domain-specific important information. This
information can be used for future decision making such as labeling of critical,
unlabeled data records and improving classification results by reducing false
positive and false negative errors. This work introduces the idea of critical nuggets,
proposes an innovative domain-independent method to measure criticality,
suggests a heuristic to reduce the search space for finding critical nuggets, and
isolates and validates critical nuggets from some real-world data sets. It seems that
only a few subsets may qualify to be critical nuggets, underlying the importance of
finding them. The proposed methodology can detect them. This work also
identifies certain properties of critical nuggets and provides experimental
validation of the properties. Experimental results also helped validate that critical
nuggets can assist in improving classification accuracies in real-world data sets.

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7. A Predictive-Reactive Method for Improving the Robustness of Real-
Time Data Services
ABSTRACT:
Supporting timely data services using fresh data in data-intensive real-time
applications, such as e-commerce and transportation management is desirable but
challenging, since the workload may vary dynamically. To control the data service
delay to be below the specified threshold, we develop a predictive as well as
reactive method for database admission control. The predictive method derives the
workload bound for admission control in a predictive manner, making no statistical
or queuing-theoretic assumptions about workloads. Also, our reactive scheme
based on formal feedback control theory continuously adjusts the database load
bound to support the delay threshold. By adapting the load bound in a proactive
fashion, we attempt to avoid severe overload conditions and excessive delays
before they occur. Also, the feedback control scheme enhances the timeliness by
compensating for potential prediction errors due to dynamic workloads. Hence, the
predictive and reactive methods complement each other, enhancing the robustness
of real-time data services as a whole. We implement the integrated approach and
several baselines in an open-source database. Compared to the tested open-loop,
feedback-only, and statistical prediction + feedback baselines representing the state
of the art, our integrated method significantly improves the average/transient delay
and real-time data service throughput.

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8. Tweet Analysis for Real-Time Event Detection and Earthquake
Reporting System Development
ABTSRACT:
Twitter has received much attention recently. An important characteristic of
Twitter is its real-time nature. We investigate the real-time interaction of events
such as earthquakes in Twitter and propose an algorithm to monitor tweets and to
detect a target event. To detect a target event, we devise a classifier of tweets based
on features such as the keywords in a tweet, the number of words, and their
context. Subsequently, we produce a probabilistic spatiotemporal model for the
target event that can find the center of the event location. We regard each Twitter
user as a sensor and apply particle filtering, which are widely used for location
estimation. The particle filter works better than other comparable methods for
estimating the locations of target events. As an application, we develop an
earthquake reporting system for use in Japan. Because of the numerous
earthquakes and the large number of Twitter users throughout the country, we can
detect an earthquake with high probability (93 percent of earthquakes of Japan
Meteorological Agency (JMA) seismic intensity scale 3 or more are detected)
merely by monitoring tweets. Our system detects earthquakes promptly and
notification is delivered much faster than JMA broadcast announcements.

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IMAGE PROCESSING
1. Discrete Wavelet Transform and Data Expansion Reduction in
Homomorphic Encrypted Domain
ABSTRACT:
Signal processing in the encrypted domain is a new technology with the goal
of protecting valuable signals from insecure signal processing. In this paper, we
propose a method for implementing discrete wavelet transform (DWT) and
multiresolution analysis (MRA) in homomorphic encrypted domain. We first
suggest a framework for performing DWT and inverse DWT (IDWT) in the
encrypted domain, then conduct an analysis of data expansion and quantization
errors under the framework. To solve the problem of data expansion, which may be
very important in practical applications, we present a method for reducing data
expansion in the case that both DWT and IDWT are performed. With the proposed
method, multilevel DWT/IDWT can be performed with less data expansion in
homomorphic encrypted domain. We propose a new signal processing procedure,
where the multiplicative inverse method is employed as the last step to limit the
data expansion. Taking a 2-D Haar wavelet transform as an example, we conduct a
few experiments to demonstrate the advantages of our method in secure image
processing. We also provide computational complexity analyses and comparisons.
To the best of our knowledge, there has been no report on the implementation of
DWT and MRA in the encrypted domain.

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2. Scanned Document Compression Using Block-Based Hybrid Video
Codec
ABSTRACT:
This paper proposes a hybrid pattern matching/transform-based compression
method for scanned documents. The idea is to use regular video interframe
prediction as a pattern matching algorithm that can be applied to document coding.
We show that this interpretation may generate residual data that can be efficiently
compressed by a transform-based encoder. The efficiency of this approach is
demonstrated using H.264/advanced video coding (AVC) as a high-quality single
and multipage document compressor. The proposed method, called advanced
document coding (ADC), uses segments of the originally independent scanned
pages of a document to create a video sequence, which is then encoded through
regular H.264/AVC. The encoding performance is unrivaled. Results show that
ADC outperforms AVC-I (H.264/AVC operating in pure intramode) and
JPEG2000 by up to 2.7 and 6.2 dB, respectively. Superior subjective quality is also
achieved.
3. Perceptual Quality-Regulable Video Coding System With Region-Based
Rate Control Scheme
ABSTRACT:
In this paper, we discuss a region-based perceptual quality-regulable H.264
video encoder system that we developed. The ability to adjust the quality of
specific regions of a source video to a predefined level of quality is an essential

27
technique for region-based video applications. We use the structural similarity
index as the quality metric for distortion-quantization modeling and develop a bit
allocation and rate control scheme for enhancing regional perceptual quality.
Exploiting the relationship between the reconstructed macroblock and the best
predicted macroblock from mode decision, a novel quantization parameter
prediction method is built and used to achieve the target video quality of the
processed macroblock. Experimental results show that the system model has only
0.013 quality error in average. Moreover, the proposed region-based rate control
system can encode video well under a bitrate constraint with a 0.1% bitrate error in
average. For the situation of the low bitrate constraint, the proposed system can
encode video with a 0.5% bit error rate in average and enhance the quality of the
target regions.
4. General Framework to Histogram-Shifting-Based Reversible Data
Hiding
ABSTRACT:
Histogram shifting (HS) is a useful technique of reversible data hiding
(RDH). With HS-based RDH, high capacity and low distortion can be achieved
efficiently. In this paper, we revisit the HS technique and present a general
framework to construct HS-based RDH. By the proposed framework, one can get a
RDH algorithm by simply designing the so-called shifting and embedding
functions. Moreover, by taking specific shifting and embedding functions, we
show that several RDH algorithms reported in the literature are special cases of this
general construction. In addition, two novel and efficient RDH algorithms are also

28
introduced to further demonstrate the universality and applicability of our
framework. It is expected that more efficient RDH algorithms can be devised
according to the proposed framework by carefully designing the shifting and
embedding functions.
5. Exploring Visual and Motion Saliency for Automatic Video Object
Extraction
ABSTRACT:
This paper presents a saliency-based video object extraction (VOE)
framework. The proposed framework aims to automatically extract foreground
objects of interest without any user interaction or the use of any training data (i.e.,
not limited to any particular type of object). To separate foreground and
background regions within and across video frames, the proposed method utilizes
visual and motion saliency information extracted from the input video. A
conditional random field is applied to effectively combine the saliency induced
features, which allows us to deal with unknown pose and scale variations of the
foreground object (and its articulated parts). Based on the ability to preserve both
spatial continuity and temporal consistency in the proposed VOE framework,
experiments on a variety of videos verify that our method is able to produce
quantitatively and qualitatively satisfactory VOE results.

29
MOBILE COMPUTING
1. Content Sharing over Smartphone-Based Delay-Tolerant Networks
ABSTRACT:
With the growing number of smartphone users, peer-to-peer ad hoc content
sharing is expected to occur more often. Thus, new content sharing mechanisms
should be developed as traditional data delivery schemes are not efficient for
content sharing due to the sporadic connectivity between smartphones. To
accomplish data delivery in such challenging environments, researchers have
proposed the use of store-carry-forward protocols, in which a node stores a
message and carries it until a forwarding opportunity arises through an encounter
with other nodes. Most previous works in this field have focused on the prediction
of whether two nodes would encounter each other, without considering the place
and time of the encounter. In this paper, we propose discover-predict-deliver as an
efficient content sharing scheme for delay-tolerant smartphone networks. In our
proposed scheme, contents are shared using the mobility information of
individuals. Specifically, our approach employs a mobility learning algorithm to
identify places indoors and outdoors. A hidden Markov model is used to predict an
individual's future mobility information. Evaluation based on real traces indicates
that with the proposed approach, 87 percent of contents can be correctly discovered
and delivered within 2 hours when the content is available only in 30 percent of
nodes in the network. We implement a sample application on commercial
smartphones, and we validate its efficiency to analyze the practical feasibility of

30
the content sharing application. Our system approximately results in a 2 percent
CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at
most.
2. Distributed Cooperative Caching in Social Wireless Networks
ABSTRACT:
This paper introduces cooperative caching policies for minimizing electronic
content provisioning cost in Social Wireless Networks (SWNET). SWNETs are
formed by mobile devices, such as data enabled phones, electronic book readers
etc., sharing common interests in electronic content, and physically gathering
together in public places. Electronic object caching in such SWNETs are shown to
be able to reduce the content provisioning cost which depends heavily on the
service and pricing dependences among various stakeholders including content
providers (CP), network service providers, and End Consumers (EC). Drawing
motivation from Amazon's Kindle electronic book delivery business, this paper
develops practical network, service, and pricing models which are then used for
creating two object caching strategies for minimizing content provisioning costs in
networks with homogenous and heterogeneous object demands. The paper
constructs analytical and simulation models for analyzing the proposed caching
strategies in the presence of selfish users that deviate from network-wide cost-
optimal policies. It also reports results from an Android phone-based prototype
SWNET, validating the presented analytical and simulation results.

31
3. Predicting Human Movement Based on Telecom's Handoff in Mobile
Networks
ABSTRACT:
Investigating human movement behavior is important for studying issues
such as prediction of vehicle traffic and spread of contagious diseases. Since
mobile telecom network can efficiently monitor the movement of mobile users, the
telecom's mobility management is an ideal mechanism for studying human
movement issues. The problem can be abstracted as follows: What is the
probability that a person at location A will move to location B after T hours. The
answer cannot be directly obtained because commercial telecom networks do not
exactly trace the movement history of every mobile user. In this paper, we show
how to use the standard outputs (handover rates, call arrival rates, call holding
time, and call traffic) measured in a mobile telecom network to derive the answer
for this problem.
4. Spatial Distribution and Channel Quality Adaptive Protocol for
Multihop Wireless Broadcast Routing in VANET
ABSTRACT:
Multihop wireless broadcast is an important component in vehicular
networks. Many applications are built on broadcast communications, so efficient
routing methods are critical for their success. Here, we develop the Distribution-
Adaptive Distance with Channel Quality (DADCQ) protocol to address this need
and show that it performs well compared to several existing multihop broadcast

32
proposals. The DADCQ protocol utilizes the distance method to select forwarding
nodes. The performance of this method depends heavily on the value of the
decision threshold, but it is difficult to choose a value that results in good
performance across all scenarios. Node density, spatial distribution pattern, and
wireless channel quality all affect the optimal value. Broadcast protocols tailored
to vehicular networking must be adaptive to variation in these factors. In this work,
we address this design challenge by creating a decision threshold function that is
simultaneously adaptive to the number of neighbors, the node clustering factor, and
the Rician fading parameter. To calculate the clustering factor, we propose using
the quadrat method of spatial analysis. The resulting DADCQ protocol is then
verified with JiST/SWANS and shown to achieve high reachability and low
bandwidth consumption in urban and highway scenarios with varying node density
and fading intensity.
5. A Robust Indoor Pedestrian Tracking System with Sparse
Infrastructure Support
ABSTRACT:
Existing approaches to indoor tracking have various limitations. Location-
fingerprinting approaches are labor intensive and vulnerable to environmental
changes. Trilateration approaches require at least three line-of-sight beacons for
coverage at any point in the service area, which results in heavy infrastructure cost.
Dead reckoning (DR) approaches rely on knowledge of the initial location and
suffer from tracking error accumulation. Despite this, we adopt DR for location
tracking because of the recent emergence of affordable hand-held devices equipped

33
with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian
tracking system that comprises of a DR subsystem implemented on a mobile phone
and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion
scheme is applied to bound the accumulated tracking error by fusing DR with
sparse range measurements. Experimental results show that the proposed system is
able to track users much better than DR alone. The system is robust even when: 1)
the initial user location is not available; 2) range updates are noisy; and 3) range
updates are intermittent, both temporally and spatially.
6. A MAC Sensing Protocol Design for Data Transmission with More
Protection to Primary Users
ABSTRACT:
MAC protocols to sense channels for data transmission have been widely
investigated for the secondary users to efficiently utilize and share the spectrum
licensed by the primary user. One important issue associated with MAC protocols
design is how the secondary users determine when and which channel they should
sense and access without causing harmful interference to the primary user. In this
paper, we jointly consider the MAC-layer spectrum sensing and channel access.
Normal Spectrum Sensing (NSS) is required to be carried out at the beginning of
each frame to determine whether the channel is idle. On detecting the available
transmission opportunity, the secondary users employ CSMA for channel
contention. The novelty is that, Fast Spectrum Sensing (FSS) is inserted after
channel contention to promptly detect the return of the primary users. This is
unlike most other MAC protocols which do not incorporate FSS. Having FSS, the

34
primary user can benefit from more protection. A concrete protocol design is
provided in this paper, and the throughput-collision tradeoff and utility-collision
tradeoff problems are formulated to evaluate its performance. Simulation results
demonstrate the efficiency of the proposed MAC protocol with FSS.
7. Cross-Layer Minimum-Delay Scheduling and Maximum-Throughput
Resource Allocation for Multiuser Cognitive Networks
ABSTRACT:
A cognitive network is considered that consists of a base station (BS)
communicating with multiple primary and secondary users. Each secondary user
can access only one of the orthogonal primary channels. A model is considered in
which the primary users can tolerate a certain average delay. A special case is also
considered in which the primary users do not suffer from any delay. A novel cross-
layer scheme is proposed in which the BS performs successive interference
cancellation and thus a secondary user can coexist with an active primary user
without adversely affecting its transmission. A scheduling algorithm is proposed
that minimizes the average packet delay of the secondary user under constraints on
the average power transmitted by the secondary user and the average packet delay
of the primary user. A resource allocation algorithm is also proposed to assign the
secondary users' channels such that the total throughput of the network is
maximized. Our results indicate that the network throughput increases significantly
by increasing the number of transmitted packets of the secondary users and/or by
allowing a small delay for the primary user packets.

35
8. ALERT: An Anonymous Location-Based Efficient Routing Protocol in
MANETs
ABSTRACT:
Mobile Ad Hoc Networks (MANETs) use anonymous routing protocols that hide
node identities and/or routes from outside observers in order to provide anonymity
protection. However, existing anonymous routing protocols relying on either hop-
by-hop encryption or redundant traffic, either generate high cost or cannot provide
full anonymity protection to data sources, destinations, and routes. The high cost
exacerbates the inherent resource constraint problem in MANETs especially in
multimedia wireless applications. To offer high anonymity protection at a low cost,
we propose an Anonymous Location-based Efficient Routing proTocol (ALERT).
ALERT dynamically partitions the network field into zones and randomly chooses
nodes in zones as intermediate relay nodes, which form a nontraceable anonymous
route. In addition, it hides the data initiator/receiver among many
initiators/receivers to strengthen source and destination anonymity protection.
Thus, ALERT offers anonymity protection to sources, destinations, and routes. It
also has strategies to effectively counter intersection and timing attacks. We
theoretically analyze ALERT in terms of anonymity and efficiency. Experimental
results exhibit consistency with the theoretical analysis, and show that ALERT
achieves better route anonymity protection and lower cost compared to other
anonymous routing protocols. Also, ALERT achieves comparable routing
efficiency to the GPSR geographical routing protocol.

36
MULTIMEDIA
1. An Adaptive Cloud Downloading Service
ABSTRACT:
Video content downloading using the P2P approach is scalable, but does not
always give good performance. Recently, subscription-based premium services
have emerged, referred to as cloud downloading. In this service, the cloud storage
and server caches user-interested content and updates the cache based on user
downloading requests. If a requested video is not in the cache, the request is held in
a waiting state until the cache is updated. We call this design server mode. An
alternative design is to let the cloud server serve all downloading requests as soon
as they arrive, behaving as a helper peer. We call this design helper mode. Our
model and analysis show that both these designs are useful for certain operating
regimes. The helper mode is good at handling a high request rate, while the server
mode is good at scaling with video population size. We design an adaptive
algorithm (AMS) to select the service mode automatically. Intuitively, AMS
switches service mode from server mode to helper mode when too many peers
request blocked movies, and vice versa. The ability of AMS to achieve good
performance in different operating regimes is validated by simulation .

37
2. Efficient Resource Provisioning and Rate Selection for Stream Mining
in a Community Cloud
ABSTRACT:
Real-time stream mining such as surveillance and personal health
monitoring, which involves sophisticated mathematical operations, is computation-
intensive and prohibitive for mobile devices due to the hardware/computation
constraints. To satisfy the growing demand for stream mining in mobile networks,
we propose to employ a cloud-based stream mining system in which the mobile
devices send via wireless links unclassified media streams to the cloud for
classification. We aim at minimizing the classification-energy cost, defined as an
affine combination of classification cost and energy consumption at the cloud,
subject to an average stream mining delay constraint (which is important in real-
time applications). To address the challenge of time-varying wireless channel
conditions without a priori information about the channel statistics, we develop an
online algorithm in which the cloud operator can dynamically adjust its resource
provisioning on the fly and the mobile devices can adapt their transmission rates to
the instantaneous channel conditions. It is proved that, at the expense of increasing
the average stream mining delay, the online algorithm achieves a classification-
energy cost that can be pushed arbitrarily close to the minimum cost achieved by
the optimal offline algorithm. Extensive simulations are conducted to validate the
analysis.

38
3. Cloud-Based Image Coding for Mobile Devices—Toward Thousands to
One Compression
ABSTRACT:
Current image coding schemes make it hard to utilize external images for
compression even if highly correlated images can be found in the cloud. To solve
this problem, we propose a method of cloud-based image coding that is different
from current image coding even on the ground. It no longer compresses images
pixel by pixel and instead tries to describe images and reconstruct them from a
large-scale image database via the descriptions. First, we describe an input image
based on its down-sampled version and local feature descriptors. The descriptors
are used to retrieve highly correlated images in the cloud and identify
corresponding patches. The down-sampled image serves as a target to stitch
retrieved image patches together. Second, the down-sampled image is compressed
using current image coding. The feature vectors of local descriptors are predicted
by the corresponding vectors extracted in the decoded down-sampled image. The
predicted residual vectors are compressed by transform, quantization, and entropy
coding. The experimental results show that the visual quality of reconstructed
images is significantly better than that of intra-frame coding in HEVC and JPEG at
thousands to one compression .

39
4. Optimizing Cloud Resources for Delivering IPTV Services Through
Virtualization
ABSTRACT:
Virtualized cloud-based services can take advantage of statistical
multiplexing across applications to yield significant cost savings. However,
achieving similar savings with real-time services can be a challenge. In this paper,
we seek to lower a provider's costs for real-time IPTV services through a
virtualized IPTV architecture and through intelligent time-shifting of selected
services. Using Live TV and Video-on-Demand (VoD) as examples, we show that
we can take advantage of the different deadlines associated with each service to
effectively multiplex these services. We provide a generalized framework for
computing the amount of resources needed to support multiple services, without
missing the deadline for any service. We construct the problem as an optimization
formulation that uses a generic cost function. We consider multiple forms for the
cost function (e.g., maximum, convex and concave functions) reflecting the cost of
providing the service. The solution to this formulation gives the number of servers
needed at different time instants to support these services. We implement a simple
mechanism for time-shifting scheduled jobs in a simulator and study the reduction
in server load using real traces from an operational IPTV network. Our results
show that we are able to reduce the load by ~24%(compared to a possible ~31.3%
as predicted by the optimization framework).

40
5. A Network and Device Aware QoS Approach for Cloud-Based Mobile
Streaming
ABSTRACT:
Cloud multimedia services provide an efficient, flexible, and scalable data
processing method and offer a solution for the user demands of high quality and
diversified multimedia. As intelligent mobile phones and wireless networks
become more and more popular, network services for users are no longer limited to
the home. Multimedia information can be obtained easily using mobile devices,
allowing users to enjoy ubiquitous network services. Considering the limited
bandwidth available for mobile streaming and different device requirements, this
study presented a network and device-aware Quality of Service (QoS) approach
that provides multimedia data suitable for a terminal unit environment via
interactive mobile streaming services, further considering the overall network
environment and adjusting the interactive transmission frequency and the dynamic
multimedia transcoding, to avoid the waste of bandwidth and terminal power.
Finally, this study realized a prototype of this architecture to validate the feasibility
of the proposed method. According to the experiment, this method could provide
efficient self-adaptive multimedia streaming services for varying bandwidth
environments.

41
NETWORK SECURITY
1. To Lie or to Comply: Defending against Flood Attacks in Disruption
Tolerant Networks
ABSTRACT:
Disruption Tolerant Networks (DTNs) utilize the mobility of nodes and the
opportunistic contacts among nodes for data communications. Due to the limitation
in network resources such as contact opportunity and buffer space, DTNs are
vulnerable to flood attacks in which attackers send as many packets or packet
replicas as possible to the network, in order to deplete or overuse the limited
network resources. In this paper, we employ rate limiting to defend against flood
attacks in DTNs, such that each node has a limit over the number of packets that it
can generate in each time interval and a limit over the number of replicas that it
can generate for each packet. We propose a distributed scheme to detect if a node
has violated its rate limits. To address the challenge that it is difficult to count all
the packets or replicas sent by a node due to lack of communication infrastructure,
our detection adopts claim-carry-and-check: each node itself counts the number of
packets or replicas that it has sent and claims the count to other nodes; the
receiving nodes carry the claims when they move, and cross-check if their carried
claims are inconsistent when they contact. The claim structure uses the pigeonhole
principle to guarantee that an attacker will make inconsistent claims which may
lead to detection. We provide rigorous analysis on the probability of detection, and

42
evaluate the effectiveness and efficiency of our scheme with extensive trace-driven
simulations.
2. Design and Implementation of TARF: A Trust-Aware Routing
Framework for WSNs
ABSTRACT:
The multihop routing in wireless sensor networks (WSNs) offers little
protection against identity deception through replaying routing information. An
adversary can exploit this defect to launch various harmful or even devastating
attacks against the routing protocols, including sinkhole attacks, wormhole attacks,
and Sybil attacks. The situation is further aggravated by mobile and harsh network
conditions. Traditional cryptographic techniques or efforts at developing trust-
aware routing protocols do not effectively address this severe problem. To secure
the WSNs against adversaries misdirecting the multihop routing, we have designed
and implemented TARF, a robust trust-aware routing framework for dynamic
WSNs. Without tight time synchronization or known geographic information,
TARF provides trustworthy and energy-efficient route. Most importantly, TARF
proves effective against those harmful attacks developed out of identity deception;
the resilience of TARF is verified through extensive evaluation with both
simulation and empirical experiments on large-scale WSNs under various
scenarios including mobile and RF-shielding network conditions. Further, we have
implemented a low-overhead TARF module in TinyOS; as demonstrated, this
implementation can be incorporated into existing routing protocols with the least

43
effort. Based on TARF, we also demonstrated a proof-of-concept mobile target
detection application that functions well against an antidetection mechanism.
3. Towards Trustworthy Resource Scheduling in Clouds
ABSTRACTS:
Managing the allocation of cloud virtual machines at physical resources is a
key requirement for the success of clouds. Current implementations of cloud
schedulers do not consider the entire cloud infrastructure neither do they consider
the overall user and infrastructure properties. This results in major security,
privacy, and resilience concerns. In this paper, we propose a novel cloud scheduler
which considers both user requirements and infrastructure properties. We focus on
assuring users that their virtual resources are hosted using physical resources that
match their requirements without getting users involved with understanding the
details of the cloud infrastructure. As a proof-of-concept, we present our prototype
which is built on OpenStack. The provided prototype implements the proposed
cloud scheduler. It also provides an implementation of our previous work on cloud
trust management which provides the scheduler with input about the trust status of
the cloud infrastructure.
4. A Hierarchical Approach for the Resource Management of Very Large
Cloud Platforms
ABSTRACT:
Worldwide interest in the delivery of computing and storage capacity as a
service continues to grow at a rapid pace. The complexities of such cloud

44
computing centers require advanced resource management solutions that are
capable of dynamically adapting the cloud platform while providing continuous
service and performance guarantees. The goal of this paper is to devise resource
allocation policies for virtualized cloud environments that satisfy performance and
availability guarantees and minimize energy costs in very large cloud service
centers. We present a scalable distributed hierarchical framework based on a
mixed-integer non-linear optimization for resource management acting at multiple
time-scales. Extensive experiments across a wide variety of configurations
demonstrate the efficiency and effectiveness of our approach.
5. CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring
ABSTRACT:
Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing
mobile communications and cloud computing technologies to provide feedback decision
support, has been considered as a revolutionary approach to improving the quality of
healthcare service while lowering the healthcare cost. Unfortunately, it also poses a
serious risk on both clients' privacy and intellectual property of monitoring service
providers, which could deter the wide adoption of mHealth technology. This paper is to
address this important problem and design a cloud-assisted privacy preserving mobile
health monitoring system to protect the privacy of the involved parties and their data.
Moreover, the outsourcing decryption technique and a newly proposed key private proxy
reencryption are adapted to shift the computational complexity of the involved parties to
the cloud without compromising clients' privacy and service providers' intellectual
property. Finally, our security and performance analysis demonstrates the effectiveness of
our proposed design.

45
NETWORKING
1. An Effective Network Traffic Classification Method with Unknown
Flow Detection
ABSTRACT:
Traffic classification technique is an essential tool for network and system
security in the complex environments such as cloud computing based environment.
The state-of-the-art traffic classification methods aim to take the advantages of
flow statistical features and machine learning techniques, however the
classification performance is severely affected by limited supervised information
and unknown applications. To achieve effective network traffic classification, we
propose a new method to tackle the problem of unknown applications in the crucial
situation of a small supervised training set. The proposed method possesses the
superior capability of detecting unknown flows generated by unknown applications
and utilizing the correlation information among real-world network traffic to boost
the classification performance. A theoretical analysis is provided to confirm
performance benefit of the proposed method. Moreover, the comprehensive
performance evaluation conducted on two real-world network traffic datasets
shows that the proposed scheme outperforms the existing methods in the critical
network environment.

46
2. Hierarchical Trust Management for Wireless Sensor Networks and its
Applications to Trust-Based Routing and Intrusion Detection
ABSTRACT:
We propose a highly scalable cluster-based hierarchical trust management
protocol for wireless sensor networks (WSNs) to effectively deal with selfish or
malicious nodes. Unlike prior work, we consider multidimensional trust attributes
derived from communication and social networks to evaluate the overall trust of a
sensor node. By means of a novel probability model, we describe a heterogeneous
WSN comprising a large number of sensor nodes with vastly different social and
quality of service (QoS) behaviors with the objective to yield "ground truth" node
status. This serves as a basis for validating our protocol design by comparing
subjective trust generated as a result of protocol execution at runtime against
objective trust obtained from actual node status. To demonstrate the utility of our
hierarchical trust management protocol, we apply it to trust-based geographic
routing and trust-based intrusion detection. For each application, we identify the
best trust composition and formation to maximize application performance. Our
results indicate that trust-based geographic routing approaches the ideal
performance level achievable by flooding-based routing in message delivery ratio
and message delay without incurring substantial message overhead. For trust-based
intrusion detection, we discover that there exists an optimal trust threshold for
minimizing false positives and false negatives. Furthermore, trust-based intrusion
detection outperforms traditional anomaly-based intrusion detection approaches in
both the detection probability and the false positive probability.

47
3. Torrents on Twitter: Explore Long-Term Social Relationships in Peer-
to-Peer Systems
ABSTRACT:
Peer-to-peer file sharing systems, most notably BitTorrent (BT), have
achieved tremendous success among Internet users. Recent studies suggest that the
long-term relationships among BT peers can be explored to enhance the
downloading performance; for example, for re-sharing previously downloaded
contents or for effectively collaborating among the peers. However, whether such
relationships do exist in real world remains unclear. In this paper, we take a first
step towards the real-world applicability of peers' long-term relationship through a
measurement based study. We find that 95% peers cannot even meet each other
again in the BT networks; therefore, most peers can hardly be organized for further
cooperation. This result contradicts to the conventional understanding based on the
observed daily arrival pattern in peer-to-peer networks. To better understand this,
we revisit the arrival of BT peers as well as their long-range dependence. We find
that the peers' arrival patterns are highly diverse; only a limited number of stable
peers have clear self-similar and periodic daily arrivals patterns. The arrivals of
most peers are, however, quite random with little evidence of long-range
dependence. To better utilize these stable peers, we start to explore peers' long-
term relationships in specific swarms instead of conventional BT networks.
Fortunately, we find that the peers in Twitter-initialized torrents have stronger
temporal locality, thus offering great opportunity for improving their degree of
sharing. Our PlanetLab experiments further indicate that the incorporation of social

48
relations remarkably accelerates the download completion time. The improvement
remains noticeable even in a hybrid system with a small set of social friends only.
4. Efficient Storage and Processing of High-Volume Network Monitoring
Data
ABSTRACT:
Monitoring modern networks involves storing and transferring huge
amounts of data. To cope with this problem, in this paper we propose a technique
that allows to transform the measurement data in a representation format meeting
two main objectives at the same time. Firstly, it allows to perform a number of
operations directly on the transformed data with a controlled loss of accuracy,
thanks to the mathematical framework it is based on. Secondly, the new
representation has a small memory footprint, allowing to reduce the space needed
for data storage and the time needed for data transfer. To validate our technique,
we perform an analysis of its performance in terms of accuracy and memory
footprint. The results show that the transformed data closely approximates the
original data (within 5% relative error) while achieving a compression ratio of
20%; storage footprint can also be gradually reduced towards the one of the state-
of-the-art compression tools, such as bzip2, if higher approximation is allowed.
Finally, a sensibility analysis show that technique allows to trade-off the accuracy
on different input fields so to accommodate for specific application needs, while a
scalability analysis indicates that the technique scales with input size spanning up
to three orders of magnitude.

49
5. An Efficient and Robust Addressing Protocol for Node
Autoconfiguration in Ad Hoc Networks
ABSTRACT:
Address assignment is a key challenge in ad hoc networks due to the lack of
infrastructure. Autonomous addressing protocols require a distributed and self-
managed mechanism to avoid address collisions in a dynamic network with fading
channels, frequent partitions, and joining/leaving nodes. We propose and analyze a
lightweight protocol that configures mobile ad hoc nodes based on a distributed
address database stored in filters that reduces the control load and makes the
proposal robust to packet losses and network partitions. We evaluate the
performance of our protocol, considering joining nodes, partition merging events,
and network initialization. Simulation results show that our protocol resolves all
the address collisions and also reduces the control traffic when compared to
previously proposed protocols.
6. Complexity Analysis and Algorithm Design for Advance Bandwidth
Scheduling in Dedicated Networks
ABSTRACT:
An increasing number of high-performance networks provision dedicated
channels through circuit switching or MPLS/GMPLS techniques to support large
data transfer. The link bandwidths in such networks are typically shared by
multiple users through advance reservation, resulting in varying bandwidth

50
availability in future time. Developing efficient scheduling algorithms for advance
bandwidth reservation has become a critical task to improve the utilization of
network resources and meet the transport requirements of application users. We
consider an exhaustive combination of different path and bandwidth constraints
and formulate four types of advance bandwidth scheduling problems, with the
same objective to minimize the data transfer end time for a given transfer request
with a prespecified data size: fixed path with fixed bandwidth (FPFB); fixed path
with variable bandwidth (FPVB); variable path with fixed bandwidth (VPFB); and
variable path with variable bandwidth (VPVB). For VPFB and VPVB, we further
consider two subcases where the path switching delay is negligible or
nonnegligible. We propose an optimal algorithm for each of these scheduling
problems except for FPVB and VPVB with nonnegligible path switching delay,
which are proven to be NP-complete and nonapproximable, and then tackled by
heuristics. The performance superiority of these heuristics is verified by extensive
experimental results in a large set of simulated networks in comparison to optimal
and greedy strategies.
7. Distortion-Aware Scalable Video Streaming to Multinetwork Clients
ABSTRACT:
We consider the problem of scalable video streaming from a server to
multinetwork clients over heterogeneous access networks, with the goal of
minimizing the distortion of the received videos. This problem has numerous
applications including: 1) mobile devices connecting to multiple licensed and ISM
bands, and 2) cognitive multiradio devices employing spectrum bonding. In this

51
paper, we ascertain how to optimally determine which video packets to transmit
over each access network. We present models to capture the network conditions
and video characteristics and develop an integer program for deterministic packet
scheduling. Solving the integer program exactly is typically not computationally
tractable, so we develop heuristic algorithms for deterministic packet scheduling,
as well as convex optimization problems for randomized packet scheduling. We
carry out a thorough study of the tradeoff between performance and computational
complexity and propose a convex programming-based algorithm that yields good
performance while being suitable for real-time applications. We conduct extensive
trace-driven simulations to evaluate the proposed algorithms using real network
conditions and scalable video streams. The simulation results show that the
proposed convex programming-based algorithm: 1) outperforms the rate control
algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about
10-15 dB higher video quality; 2) reduces average delivery delay by over 90%
compared to DCCP; 3) results in higher average video quality of 4.47 and 1.92 dB
than the two developed heuristics; 4) runs efficiently, up to six times faster than the
best-performing heuristic; and 5) does indeed provide service differentiation
among users.
8. Exploring the Design Space of Multichannel Peer-to-Peer Live Video
Streaming Systems
ABSTRACT:
Most of the commercial peer-to-peer (P2P) video streaming deployments
support hundreds of channels and are referred to as multichannel systems. Recent

52
research studies have proposed specific protocols to improve the streaming quality
for all channels by enabling cross-channel cooperation among multiple channels.
In this paper, we focus on the following fundamental problems in designing
cooperating multichannel systems: 1) what are the general characteristics of
existing and potential designs? and 2) under what circumstances should a particular
design be used to achieve the desired streaming quality with the lowest
implementation complexity? To answer the first question, we propose simple
models based on linear programming and network-flow graphs for three general
designs, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-
aware bandwidth allocation Approach (PCA), and Active Channel-aware
bandwidth allocation Approach (ACA), which provide insight into understanding
the key characteristics of cross-channel resource sharing. For the second question,
we first develop closed-form results for two-channel systems. Then, we use
extensive numerical simulations to compare the three designs for various peer
population distributions, upload bandwidth distributions, and channel structures.
Our analytical and simulation results show that: 1) the NBA design can rarely
achieve the desired streaming quality in general cases; 2) the PCA design can
achieve the same performance as the ACA design in general cases; and 3) the ACA
design should be used for special applications.

53
PARALLEL AND DISTRIBUTED SYSTEM
1. Efficient Resource Mapping Framework over Networked Clouds via
Iterated Local Search-Based Request Partitioning
ABSTRACT:
The cloud represents a computing paradigm where shared configurable
resources are provided as a service over the Internet. Adding intra- or intercloud
communication resources to the resource mix leads to a networked cloud
computing environment. Following the cloud infrastructure as a Service paradigm
and in order to create a flexible management framework, it is of paramount
importance to address efficiently the resource mapping problem within this
context. To deal with the inherent complexity and scalability issue of the resource
mapping problem across different administrative domains, in this paper a
hierarchical framework is described. First, a novel request partitioning approach
based on Iterated Local Search is introduced that facilitates the cost-efficient and
online splitting of user requests among eligible cloud service providers (CPs)
within a networked cloud environment. Following and capitalizing on the outcome
of the request partitioning phase, the embedding phase-where the actual mapping
of requested virtual to physical resources is performed can be realized through the
use of a distributed intracloud resource mapping approach that allows for efficient
and balanced allocation of cloud resources. Finally, a thorough evaluation of the
proposed overall framework on a simulated networked cloud environment is

54
provided and critically compared against an exact request partitioning solution as
well as another common intradomain virtual resource embedding solution.
2. A Highly Practical Approach toward Achieving Minimum Data Sets
Storage Cost in the Cloud
ABSTRACT:
Massive computation power and storage capacity of cloud computing
systems allow scientists to deploy computation and data intensive applications
without infrastructure investment, where large application data sets can be stored
in the cloud. Based on the pay-as-you-go model, storage strategies and
benchmarking approaches have been developed for cost-effectively storing large
volume of generated application data sets in the cloud. However, they are either
insufficiently cost-effective for the storage or impractical to be used at runtime. In
this paper, toward achieving the minimum cost benchmark, we propose a novel
highly cost-effective and practical storage strategy that can automatically decide
whether a generated data set should be stored or not at runtime in the cloud. The
main focus of this strategy is the local-optimization for the tradeoff between
computation and storage, while secondarily also taking users' (optional)
preferences on storage into consideration. Both theoretical analysis and simulations
conducted on general (random) data sets as well as specific real world applications
with Amazon's cost model show that the cost-effectiveness of our strategy is close

55
to or even the same as the minimum cost benchmark, and the efficiency is very
high for practical runtime utilization in the cloud.
3. Optimal Multiserver Configuration for Profit Maximization in Cloud
Computing
ABSTRACT:
As cloud computing becomes more and more popular, understanding the
economics of cloud computing becomes critically important. To maximize the
profit, a service provider should understand both service charges and business
costs, and how they are determined by the characteristics of the applications and
the configuration of a multiserver system. The problem of optimal multiserver
configuration for profit maximization in a cloud computing environment is studied.
Our pricing model takes such factors into considerations as the amount of a
service, the workload of an application environment, the configuration of a
multiserver system, the service-level agreement, the satisfaction of a consumer, the
quality of a service, the penalty of a low-quality service, the cost of renting, the
cost of energy consumption, and a service provider's margin and profit. Our
approach is to treat a multiserver system as an M/M/m queuing model, such that
our optimization problem can be formulated and solved analytically. Two server
speed and power consumption models are considered, namely, the idle-speed
model and the constant-speed model. The probability density function of the
waiting time of a newly arrived service request is derived. The expected service
charge to a service request is calculated. The expected net business gain in one unit

56
of time is obtained. Numerical calculations of the optimal server size and the
optimal server speed are demonstrated.
4. Anchor: A Versatile and Efficient Framework for Resource
Management in the Cloud
ABSTRACT:
We present Anchor, a general resource management architecture that uses
the stable matching framework to decouple policies from mechanisms when
mapping virtual machines to physical servers. In Anchor, clients and operators are
able to express a variety of distinct resource management policies as they deem fit,
and these policies are captured as preferences in the stable matching framework.
The highlight of Anchor is a new many-to-one stable matching theory that
efficiently matches VMs with heterogeneous resource needs to servers, using both
offline and online algorithms. Our theoretical analyses show the convergence and
optimality of the algorithm. Our experiments with a prototype implementation on a
20-node server cluster, as well as large-scale simulations based on real-world
workload traces, demonstrate that the architecture is able to realize a diverse set of
policy objectives with good performance and practicality.
5. Load Rebalancing for Distributed File Systems in Clouds
ABSTRACT:
Distributed file systems are key building blocks for cloud computing
applications based on the MapReduce programming paradigm. In such file
systems, nodes simultaneously serve computing and storage functions; a file is

57
partitioned into a number of chunks allocated in distinct nodes so that MapReduce
tasks can be performed in parallel over the nodes. However, in a cloud computing
environment, failure is the norm, and nodes may be upgraded, replaced, and added
in the system. Files can also be dynamically created, deleted, and appended. This
results in load imbalance in a distributed file system; that is, the file chunks are not
distributed as uniformly as possible among the nodes. Emerging distributed file
systems in production systems strongly depend on a central node for chunk
reallocation. This dependence is clearly inadequate in a large-scale, failure-prone
environment because the central load balancer is put under considerable workload
that is linearly scaled with the system size, and may thus become the performance
bottleneck and the single point of failure. In this paper, a fully distributed load
rebalancing algorithm is presented to cope with the load imbalance problem. Our
algorithm is compared against a centralized approach in a production system and a
competing distributed solution presented in the literature. The simulation results
indicate that our proposal is comparable with the existing centralized approach and
considerably outperforms the prior distributed algorithm in terms of load
imbalance factor, movement cost, and algorithmic overhead. The performance of
our proposal implemented in the Hadoop distributed file system is further
investigated in a cluster environment.

58
SERVICE COMPUTING
1. Optimization of Resource Provisioning Cost in Cloud Computing
ABSTRACT:
In cloud computing, cloud providers can offer cloud consumers two
provisioning plans for computing resources, namely reservation and on-demand
plans. In general, cost of utilizing computing resources provisioned by reservation
plan is cheaper than that provisioned by on-demand plan, since cloud consumer has
to pay to provider in advance. With the reservation plan, the consumer can reduce
the total resource provisioning cost. However, the best advance reservation of
resources is difficult to be achieved due to uncertainty of consumer's future
demand and providers' resource prices. To address this problem, an optimal cloud
resource provisioning (OCRP) algorithm is proposed by formulating a stochastic
programming model. The OCRP algorithm can provision computing resources for
being used in multiple provisioning stages as well as a long-term plan, e.g., four
stages in a quarter plan and twelve stages in a yearly plan. The demand and price
uncertainty is considered in OCRP. In this paper, different approaches to obtain the
solution of the OCRP algorithm are considered including deterministic equivalent
formulation, sample-average approximation, and Benders decomposition.
Numerical studies are extensively performed in which the results clearly show that
with the OCRP algorithm, cloud consumer can successfully minimize total cost of
resource provisioning in cloud computing environments.

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2. Interacting with the SOA-Based Internet of Things: Discovery, Query,
Selection, and On-Demand Provisioning of Web Services
ABSTRACT:
The increasing usage of smart embedded devices in business blurs the line
between the virtual and real worlds. This creates new opportunities to build
applications that better integrate real-time state of the physical world, and hence,
provides enterprise services that are highly dynamic, more diverse, and efficient.
Service-Oriented Architecture (SOA) approaches traditionally used to couple
functionality of heavyweight corporate IT systems, are becoming applicable to
embedded real-world devices, i.e., objects of the physical world that feature
embedded processing and communication. In such infrastructures, composed of
large numbers of networked, resource-limited devices, the discovery of services
and on-demand provisioning of missing functionality is a significant challenge. We
propose a process and a suitable system architecture that enables developers and
business process designers to dynamically query, select, and use running instances
of real-world services (i.e., services running on physical devices) or even deploy
new ones on-demand, all in the context of composite, real-world business
applications.

Java Titles & abstracts 2013-2014

Java Titles & abstracts 2013-2014

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Java Titles & abstracts 2013-2014