Cloud Computing & Mobile Computing & Networking Related Titles and Abstract:
1. Guest Editorial: Recommendation Techniques for Services Computing and Cloud
Computing
Abstract:As the number of Web services surges rapidly, recommending the right service for
various users on demand has become one of the most challenging research issues in the fields
of services computing and cloud computing. This special issue focuses on the
recommendation techniques for services computing and cloud computing including classic
recommendation algorithms for services computing and cloud computing, emerging
recommendation techniques for service selection and composition, and applications of
recommendation techniques in composition of complex service mashups, ad-hoc social
networks, and green cloud environment.
The papers introduced in this special issue illustrate the efficiency and effectiveness
of services computing and cloud computing, while demonstrating a variety of challenges that
arise. It is expected that this special issue, as a whole, will provide integrated and synthesized
view of the current state of the art, identify key challenges, possible research directions and
opportunities for investigation, and promote community-building efforts among researchers
and practitioners in the related fields.
2. MobiContext: A Context-aware Cloud-Based Venue Recommendation Framework
Abstract:In recent years, recommendation systems have seen significant evolution in the field
of knowledge engineering. Most of the existing recommendation systems based their models
on collaborative filtering approaches that make them simple to implement. However,
performance of most of the existing collaborative filtering-based recommendation system
suffers due to the challenges, such as: (a) cold start, (b) data sparseness, and (c) scalability.
Moreover, recommendation problem is often characterized by the presence of many
conflicting objectives or decision variables, such as users’ preferences and venue closeness.
In this paper, we proposed MobiContext, a hybrid cloud-based Bi-Objective
Recommendation Framework (BORF) for mobile social networks.
The MobiContext utilizes multi-objective optimization techniques to generate
personalized recommendations. To address the issues pertaining to cold start and data
sparseness, the BORF performs data pre-processing by using the Hub-Average (HA)
inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for scalar
optimization and an evolutionary algorithm (NSGA-II) is applied for vector optimization to
provide optimal suggestions to the users about a venue. The results of comprehensive
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experiments on a large-scale real dataset confirm the accuracy of the proposed
recommendation framework.
3. A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing
Services
Abstract:In modern societies, the number of mobile users has dramatically risen in recent
years. In this paper, an efficient authentication scheme for distributed mobile cloud
computing services is proposed. The proposed scheme provides security and convenience for
mobile users to access multiple mobile cloud computing services from multiple service
providers using only a single private key. The security strength of the proposed scheme is
based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the
scheme supports mutual authentication, key exchange, user anonymity, and user untraced
ability. From system implementation point of view, verification tables are not required for the
trusted smart card generator (SCG) service and cloud computing service providers when
adopting the proposed scheme.
In consequence, this scheme reduces the usage of memory spaces on these
corresponding service providers. In one mobile user authentication session, only the targeted
cloud service provider needs to interact with the service requestor (user). The trusted SCG
serves as the secure key distributor for distributed cloud service providers and mobile clients.
In the proposed scheme, the trusted SCG service is not involved in individual user
authentication process. With this design, our scheme reduces authentication processing time
required by communication and computation between cloud service providers and traditional
trusted third party service. Formal security proof and performance analyses are conducted to
show that the scheme is both secure and efficient.
4. Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks
Integrated with Mobile Cloud Computing
Abstract:Recently, much research has proposed to integrate mobile cloud computing (MCC)
with wireless sensor networks (WSNs) so that powerful cloud computing can be exploited to
process the data gathered by ubiquitous WSNs and share the results with mobile users.
However, all current MCC-WSN integration schemes ignore the following two observations:
1) the specific data mobile users request usually depend on the current locations of mobile
users 2) most sensors are usually equipped with non-rechargeable batteries with limited
energy.
In this paper, motivated by these two observations, two novel collaborative locationbased sleep scheduling (CLSS) schemes are proposed for WSNs integrated with MCC. Based
on the locations of mobile users, CLSS dynamically determines the awake or asleep status of
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each sensor node to reduce energy consumption of the integrated WSN. Particularly, CLSS1
focuses on maximizing the energy consumption saving of the integrated WSN while CLSS2
considers also the scalability and robustness of the integrated WSN. Theoretical and
simulation results show that for WSNs integrated with MCC, both CLSS1 and CLSS2 can
prolong the WSN lifetime while still satisfying the data requests of mobile users.
5. Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic
Wireless Channel
Abstract:This paper investigates collaborative task execution between a mobile device and a
cloud clone for mobile applications under a stochastic wireless channel. A mobile application
is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud
clone. We aim to minimize the energy consumption on the mobile device while meeting a
time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative
task execution as a constrained shortest path problem. We derive a one-climb policy by
characterizing the optimal solution and then propose an enumeration algorithm for the
collaborative task execution in polynomial time.
Further, we apply the LARAC algorithm to solving the optimization problem
approximately, which has lower complexity than the enumeration algorithm. Simulation
results show that the approximate solution of the LARAC algorithm is close to the optimal
solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline,
which is transformed to hard deadline by Markov inequality. Moreover, compared to the
local execution and the remote execution, the collaborative task execution can significantly
save the energy consumption on the mobile device, prolonging its battery life.
6. Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud
Abstract:Despite the advances in hardware for hand-held mobile devices, resource-intensive
applications (e.g., video and image storage and processing or map-reduce type) still remain
off bounds since they require large computation and storage capabilities. Recent research has
attempted to address these issues by employing remote servers, such as clouds and peer
mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent
topology changes because of node failure/unavailability and mobility as in a mobile cloud),
however, challenges of reliability and energy efficiency remain largely unaddressed.
To the best of our knowledge, we are the first to address these challenges in an
integrated manner for both data storage and processing in mobile cloud, an approach we call
k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in
the most energy-efficient way, as long as k out of n remote servers are accessible. Through a
real system implementation we prove the feasibility of our approach. Extensive simulations
demonstrate the fault tolerance and energy efficiency performance of our framework in larger
scale networks.
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7. Joint Cloud and Wireless Networks Operations in Mobile Cloud Computing
Environments with Telecom Operator Cloud
Abstract:In mobile cloud computing systems, cloud computing has a significant impact on
wireless networks. Cloud computing and wireless networks have traditionally been addressed
separately in the literature. In this paper, we jointly study the operations of cloud computing
and wireless networks in mobile computing environments, where the objective is to improve
the end-to-end performances of cloud mobile media delivered through mobile cloud
computing systems. Unlike most existing studies on wireless networks, where only the
spectrum efficiency is considered, we consider not only the spectrum efficiency in wireless
networks but also the pricing information in the cloud, based on which power allocation and
interference management in the wireless networks are performed.
We formulate the problems encountered in the operations of mobile cloud computing
environments, including determining the price to charge for media services, resource
allocation, and interference management, as a Stackelberg game model. Moreover, we extend
this game model with multiple players through network virtualization technology, and adopt
the replicator dynamics method to solve the evolutionary game between the different groups
of small cells. Furthermore, a backward induction method is used to analyze the proposed
Stackelberg game. Simulation results are presented to show the effectiveness of the proposed
techniques.
8. Joint Optimal Data Rate and Power Allocation in Lossy Mobile Ad Hoc Networks
with Delay-Constrained Traffics
Abstract:In this paper, we consider lossy mobile ad hoc networks where the data rate of a given
flow becomes lower and lower along its routing path. One of the main challenges in lossy
mobile ad hoc networks is how to achieve the conflicting goal of increased network utility
and reduced power consumption, while without following the instantaneous state of a fading
channel. To address this problem, we propose a cross-layer rate-effective network utility
maximization (RENUM) framework by taking into account the lossy nature of wireless links
and the constraints of rate outage probability and average delay. In the proposed framework,
the utility is associated with the effective rate received at the destination node of each flow
instead of the injection rate at the source of the flow.
We then present a distributed joint transmission rate, link power and average delay
control algorithm, in which explicit broadcast message passing is required for power
allocation algorithm. Motivated by the desire of power control devoid of message passing, we
give a near-optimal power-allocation scheme that makes use of autonomous SINR
measurements at each link and enjoys a fast convergence rate. The proposed algorithm is
shown through numerical simulations to outperform other network utility maximization
algorithms without rate outage probability/average delay constraints, leading to a higher
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effective rate, lower power consumption and delay. Furthermore, we conduct extensive
network-wide simulations in NS-2 simulator to evaluate the performance of the algorithm in
terms of throughput, delay, packet delivery ratio and fairness.
9. A Distributed Three-hop Routing Protocol to Increase the Capacity of Hybrid
Wireless Networks
Abstract:Hybrid wireless networks combining the advantages of both mobile ad-hoc
networks and infrastructure wireless networks have been receiving increased attention
due to their ultra-high performance. An efficient data routing protocol is important in
such networks for high network capacity and scalability. However, most routing
protocols for these networks simply combine the ad-hoc transmission mode with the
cellular transmission mode, which inherits the drawbacks of ad-hoc transmission. This
paper presents a Distributed Three-hop Routing protocol (DTR) for hybrid wireless
networks. To take full advantage of the widespread base stations, DTR divides a
message data stream into segments and transmits the segments in a distributed
manner.
It makes full spatial reuse of a system via its high speed ad-hoc interface and
alleviates mobile gateway congestion via its cellular interface. Furthermore, sending
segments to a number of base stations simultaneously increases throughput and makes
full use of widespread base stations. In addition, DTR significantly reduces overhead
due to short path lengths and the elimination of route discovery and maintenance.
DTR also has a congestion control algorithm to avoid overloading base stations.
Theoretical analysis and simulation results show the superiority of DTR in comparison
with other routing protocols in terms of throughput capacity, scalability and mobility
resilience. The results also show the effectiveness of the congestion control algorithm
in balancing the load between base stations.
10. A Privacy-Preserving Framework for Managing Mobile Ad Requests and Billing
Information
Abstract:Organizations are starting to realize the significant value of advertising on mobile
devices, and a number of systems have been developed to exploit this opportunity. From a
privacy perspective, practically all systems developed so far are based either on a trusted
third-party model or on a generalized architecture. We propose a system for delivering
context, location, time, and preference-aware advertisements to mobiles with a novel
architecture to preserve privacy.
The main adversary in our model is the server distributing the ads, which is trying to
identify users and track them, and to a lesser extent, other peers in the wireless network.
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When a node is interested in an ad, it forms a group of nearby nodes seeking ads and willing
to cooperate to achieve privacy. Peers combine their interests using a shuffling mechanism in
an ad-hoc network and send them through a primary peer to the ad-server. In this way,
preferences are masqueraded to request custom ads, which are then distributed by the primary
peer. Another mechanism is proposed to implement the billing process without disclosing
user identities.
11. Context-Aware QoE Modelling, Measurement, and Prediction in Mobile
Computing Systems
Abstract:Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human
user-related metrics will be the key success factor for current and future mobile computing
systems. QoE measurement and prediction are complex tasks as they may involve a large
parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a
few. These tasks necessitate the development of practical context-aware QoE models that
efficiently determine relationships between user context and QoE parameters. In this paper,
we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for
QoE modelling, measurement, and prediction.
We address the challenge of QoE measurement and prediction where each QoE
parameter can be measured on a different scale and may involve different units of
measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to
measure and predict users’ QoE under uncertainty. We validate CaQoEM using extensive
experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM
correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall
accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network
conditions such as vertical handoffs, wireless signal fading and wireless network congestion.
12. Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless
Sensor Networks
Abstract:Applications running on the same Wireless Sensor Network (WSN) platform usually
have different Quality of Service (QoS) requirements. Two basic requirements are low delay
and high data integrity. However, in most situations, these two requirements cannot be
satisfied simultaneously.
In this paper, based on the concept of potential in physics, we propose IDDR, a
multi-path dynamic routing algorithm, to resolve this conflict. By constructing a virtual
hybrid potential field, IDDR separates packets of applications with different QoS
requirements according to the weight assigned to each packet, and routes them towards the
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sink through different paths to improve the data fidelity for integrity-sensitive applications as
well as reduce the end-to-end delay for delay-sensitive ones. Using the Lyapunov drift
technique, we prove that IDDR is stable. Simulation results demonstrate that IDDR provides
data integrity and delay differentiated services.
13. Friendbook: A Semantic-Based Friend Recommendation System for Social
Networks
Abstract:Existing social networking services recommend friends to users based on their social
graphs, which may not be the most appropriate to reflect a user’s preferences on friend
selection in real life. In this paper, we present Friendbook, a novel semantic-based friend
recommendation system for social networks, which recommends friends to users based on
their life styles instead of social graphs. By taking advantage of sensor-rich smartphones,
Friendbook discovers life styles of users from user-centric sensor data, measures the
similarity of life styles between users, and recommends friends to users if their life styles
have high similarity. Inspired by text mining, we model a user’s daily life as life documents,
from which his/her life styles are extracted by using the Latent Dirichlet Allocation
algorithm.
We further propose a similarity metric to measure the similarity of life styles between
users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon
receiving a request, Friendbook returns a list of people with highest recommendation scores
to the query user. Finally, Friendbook integrates a feedback mechanism to further improve
the recommendation accuracy. We have implemented Friendbook on the Android-based
smartphones, and evaluated its performance on both small-scale experiments and large-scale
simulations. The results show that the recommendations accurately reflect the preferences of
users in choosing friends.
14. Influence Maximization on Large-Scale Mobile Social Network: A Divide-andConquer Method
Abstract:With the proliferation of mobile devices and wireless technologies, mobile social
network systems are increasingly available. A mobile social network plays an essential role
as the spread of information and influence in the form of “word-of-mouth”. It is a
fundamental issue to find a subset of influential individuals in a mobile social network such
that targeting them initially (e.g., to adopt a new product) will maximize the spread of the
influence (further adoptions of the new product). The problem of finding the most influential
nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable
approximation guarantees can give good approximation; However, it is computationally
expensive, if not prohibitive, to run the greedy algorithm on a large mobile social network. In
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this paper, a divide-and-conquer strategy with parallel computing mechanism has been
adopted.
We first propose an algorithm called Community-based Greedy algorithm for mining
top-K influential nodes. It encompasses two components: dividing the large-scale mobile
social network into several communities by taking into account information diffusion and
selecting communities to find influential nodes by a dynamic programming. Then, to further
improve the performance, we parallelize the influence propagation based on communities and
consider the influence propagation crossing communities. Also, we give precision analysis to
show approximation guarantees of our models. Experiments on real large-scale mobile social
networks show that the proposed methods are much faster than previous algorithms,
meanwhile, with high accuracy.
15. Modelling and Analysis of Communication Traffic Heterogeneity in Opportunistic
Networks
Abstract:In opportunistic networks, direct communication between mobile devices is used to
extend the set of services accessible through cellular or Wi-Fi networks. Mobility patterns
and their impact in such networks have been extensively studied. In contrast, this has not
been the case with communication traffic patterns, where homogeneous traffic between all
nodes is usually assumed. This assumption is generally not true, as node mobility and social
characteristics can significantly affect the end-to-end traffic demand between them. To this
end, in this paper we explore the joint effect of traffic patterns and node mobility on the
performance of popular forwarding mechanisms, both analytically and through simulations.
Among the different insights stemming from our analysis, we identify conditions
under which heterogeneity renders the added value of using extra relays more/less useful.
Furthermore, we confirm the intuition that an increasing amount of heterogeneity closes the
performance gap between different forwarding policies, making end to- end routing more
challenging in some cases, or less necessary in others. To our best knowledge, this is the first
effort to model, analyze, and quantify effects of traffic heterogeneity. We believe this is an
important step towards better protocol design and evaluation of the feasibility of applications
in opportunistic networks.
16.Towards Information Diffusion in Mobile Social Networks
Abstract:The emerging of mobile social networks opens opportunities for viral marketing.
However, before fully utilizing mobile social networks as a platform for viral marketing,
many challenges have to be addressed. In this paper, we address the problem of identifying a
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small number of individuals through whom the information can be diffused to the network as
soon as possible, referred to as the diffusion minimization problem. Diffusion minimization
under the probabilistic diffusion model can be formulated as an asymmetric k- center
problem which is NP-hard, and the best known approximation algorithm for the asymmetric
k-center problem has approximation ratio of log
n and time
complexity O(n5). Clearly, the performance and the time complexity of the approximation
algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem,
we propose a community based algorithm and a distributed set-cover algorithm.
The performance of the proposed algorithms is evaluated by extensive experiments on
both synthetic networks and a real trace. The results show that the community based
algorithm has the best performance in both synthetic networks and the real trace compared to
existing algorithms, and the distributed set-cover algorithm outperforms the approximation
algorithm in the real trace in terms of diffusion time.
17. Energy-Efficient Intrusion Detection and Mitigation for Networked Control Systems
Security
Abstract:This paper proposes an energy-efficient security aware architecture for wireless
control systems to be used in factory automation. We face deception attacks that corrupt
commands and measurements in a smart way and with intermittent behaviour to produce the
highest damage without being discovered. The intrusion is hard to distinguish from normal
disturbance. Furthermore, protection against attacks is energy-consuming and it would be
desirable to activate protection only when needed.
We pose packet-based selective encryption to reduce energy consumption, and to
detect when an attack starts and ends. Since energy consumption depends also on packet
transmission rate, especially during attacks, we also propose to adapt it according to
instantaneous control performance.
Data Mining Related Titles and Abstract:
18. Accelerated Continuous Conditional Random Fields for Load Forecasting
Abstract:Increasingly, aiming to contain their rapidly growing energy expenditures,
commercial buildings are equipped to respond to utility’s demand and price signals. Such
smart energy consumption, however, heavily relies on accurate short-term energy load
forecasting, such as hourly predictions for the next n (n>=2 hours. To attain sufficient
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accuracy for these predictions, it is important to exploit the relationships among the n
estimated outputs. This paper treats such multi-steps ahead regression task as a sequence
labeling (regression) problem, and adopts the continuous conditional random fields (CCRF)
to explicitly model these interconnected outputs. In particular, we improve the CCRF’s
computation complexity and predictive accuracy with two novel strategies. First, we employ
two tridiagonal matrix computation techniques to significantly speed up the CCRF’s training
and inference. These techniques tackle the cubic computational cost required by the matrix
inversion calculations in the training and inference of the CCRF, resulting in linear
complexity for these matrix operations. Second, we address the CCRF’s weak feature
constraint problem with a novel multi-target edge function, thus boosting the CCRF’s
predictive performance.
The proposed multi-target feature is able to convert the relationship of related outputs
with continuous values into a set of “sub-relationships”, each providing more specific feature
constraints for the interplays of the related outputs. We applied the proposed approach to two
real-world energy load prediction systems: one for electricity demand and another for gas
usage. Our experimental results show that the proposed strategy can meaningfully reduce the
predictive error for the two systems, in terms of mean absolute percentage error and root
mean square error, when compared with three benchmarking methods. Promisingly, the
relative error reduction achieved by our CCRF model was up to 50 percent.
19. Anonymizing Collections of Tree-Structured Data
Abstract:Collections of real-world data usually have implicit or explicit structural relations. For
example, databases link records through foreign keys, and XML documents express
associations between different values through syntax. Privacy preservation, until now, has
focused either on data with a very simple structure, e.g. relational tables, or on data with very
complex structure e.g. social network graphs, but has ignored intermediate cases, which are
the most frequent in practice. In this work, we focus on tree structured data. Such data stem
from various applications, even when the structure is not directly reflected in the syntax, e.g.
XML documents. A characteristic case is a database where information about a single person
is scattered amongst different tables that are associated through foreign keys. The paper
defines kðm;nÞ-anonymity, which provides protection against identity disclosure and
proposes a greedy anonymization heuristic that is able to sanitize large datasets. The
algorithm and the quality of the anonymization are evaluated experimentally.
20. APP Relationship Calculation: An Iterative Process
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Abstract:Today, plenty of apps are released to enable users to make the best use of their cell
phones. Facing the large amount of apps, app retrieval and app recommendation become
important, since users can easily use them to acquire their desired apps. To obtain highquality retrieval and recommending results, it needs to obtain the precise app relationship
calculating results. Unfortunately, the recent methods are conducted mostly relying on user’s
log or app’s description, which can only detect whether two apps are downloaded, installed
meanwhile or provide similar functions or not. In fact, apps contain many general
relationships other than similarity, such as one app needs another app as its tool. These
relationships cannot be dug via user’s log or app’s description. Reviews contain user’s
viewpoint and judgment to apps, thus they can be used to calculate relationship between apps.
To use reviews, this paper proposes an iterative process by combining review similarity and
app relationship together. Experimental results demonstrate that via this iterative process,
relationship between apps can be calculated exactly. Furthermore, this process is improved in
two aspects. One is to obtain excellent results even with weak initialization. The other is to
apply matrix product to reduce running time.
21. CrowdOp: Query Optimization for Declarative Crowdsourcing Systems
Abstract:-
We study the query optimization problem in declarative crowdsourcing systems.
Declarative crowdsourcing is designed to hide the complexities and relieve the user of the
burden of dealing with the crowd. The user is only required to submit an SQL-like query and
the system takes the responsibility of compiling the query, generating the execution plan and
evaluating in the crowdsourcing marketplace. A given query can have many alternative
execution plans and the difference in crowdsourcing cost between the best and the worst
plans may be several orders of magnitude.
Therefore, as in relational database systems, query optimization is important to
crowdsourcing systems that provide declarative query interfaces. In this paper, we propose
CROWDOP, a cost-based query optimization approach for declarative crowdsourcing
systems. CROWDOP considers both cost and latency in query optimization objectives and
generates query plans that provide a good balance between the cost and latency. We develop
efficient algorithms in t he CROWDOP for optimizing three types of queries: selection
queries, join queries, and complex selection-join queries. We validate our approach via
extensive experiments by simulation as well as with the real crowd on Amazon Mechanical
Turk.
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22. Efficient Motif Discovery for Large-Scale Time Series in
Healthcare
Abstract:—
Analyzing time series data can reveal the temporal behaviour of the underlying
mechanism producing the data. Time series motifs, which are similar sub-sequences or
frequently occur-ring patterns, have significant meanings for researchers especially in medical
domain. With the fast growth of time series data, traditional methods for motif discovery
are inefficient and not applicable to large-scale data.
This work proposes an efficient Motif Discovery method for Large-scale time
series (MDLats). By computing standard motifs, MDLats eliminates a majority of redundant
computation in the related arts and reuses existing information to the maximum. All the
motif types and sub-sequences are generated for subsequent analysis and classification. Our
system is implemented on a Hadoop platform and deployed in a hospital for clinical
electrocardiography classification. The experiments on real-world healthcare data show
that MDLats outperform the state-of-the-art methods even in large time series.
23. Relational Collaborative Topic Regression for Recommender Systems
Abstract:Due to its successful application in recommender systems, collaborative filtering (CF)
has become a hot research topic in data mining and information retrieval. In traditional CF
methods, only the feedback matrix, which contains either explicit feedback (also called
ratings) or implicit feedback on the items given by users, is used for training and prediction.
Typically, the feedback matrix is sparse, which means that most users interact with few
items. Due to this sparsity problem, traditional CF with only feedback information will suffer
from unsatisfactory performance. Recently, many researchers have proposed to utilize
auxiliary information, such as item content (attributes), to alleviate the data sparsity problem
in CF.
Collaborative topic regression (CTR) is one of these methods which has achieved
promising performance by successfully integrating both feedback information and item
content information. In many real applications, besides the feedback and item content
information, there may exist relations also known as networks) among the items which can be
helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model
called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly
integrating the user-item feedback information, item content information, and network
structure among items into the same model. Experiments on real-world datasets show that our
model can achieve better prediction accuracy than the state-of-the-art methods with lower
empirical training time. Moreover, RCTR can learn good interpretable latent structures which
are useful for recommendation.
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24. Robust Laser Speckle Authentication System through Data Mining
Techniques
Abstract:—
This paper proposes a speckle image recognition method using data mining
techniques to ensure speckle identification system feasible for authentication. This is an
interdisciplinary method that integrates the researches of optics, data mining, and image
processing. Because objects have unique but imperfect surfaces, their laser speckle is
capable of providing suitable identifiable features for authentication. In our method,
matching points among speckle images acquired from one plastic card are extracted by
scale-invariant feature transform (SIFT).
The spatial relations among the matching points are then transformed to 9
direction lower triangular (9DLT) representations. Then, the Apriori algorithm mines
frequent patterns so a useful association rule is obtained as the feature to identify the
similarity between each of the speckle images for the purpose of authenticity verification.
The proposed method is especially robust in the cases of card displacement and luminance
change resulted from laser attenuation. Experimental results show that the proposed
method has promising results and outperforms existing methods in identification accuracy.
25. Privacy-Preserving and Truthful Detection of Packet Dropping Attacks in Wireless
Ad Hoc Networks
Abstract:—
Link error and malicious packet dropping are two sources for packet losses in multihop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the
network, we are interested in determining whether the losses are caused by link errors only,
or by the combined effect of link errors and malicious drop. We are especially interested in
the insider-attack case, whereby malicious nodes that are part of the route exploit their
knowledge of the communication context to selectively drop a small amount of packets
critical to the network performance. Because the packet dropping rate in this case is
comparable to the channel error rate, conventional algorithms that are based on detecting the
packet loss rate cannot achieve satisfactory detection accuracy.
To improve the detection accuracy, we propose to exploit the correlations between
lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a
homomorphic linear authenticator (HLA) based public auditing architecture that allows the
detector to verify the truthfulness of the packet loss information reported by nodes. This
construction is privacy preserving, collusion proof, and incurs low communication and
storage overheads. To reduce the computation overhead of the baseline scheme, a packetblock-based mechanism is also proposed, which allows one to trade detection accuracy for
lower computation complexity. Through extensive simulations, we verify that the proposed
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mechanisms achieve significantly better detection accuracy than conventional methods such
as a maximum-likelihood based detection.
26. Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative
Ordinal Optimization
Abstract:—
The scheduling of multitask jobs on clouds is an NP-hard problem. The problem
becomes even worse when complex workflows are executed on elastic clouds, such as
Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high
overhead of generating optimal schedules, especially for real-time applications with
dynamic workloads. In this work, a new iterative ordinal optimization (IOO) method is
proposed. The ordinal optimization method is applied in each iteration to achieve suboptimal schedules. IOO aims at generating more efficient schedules from a global
perspective over a long period. We prove through overhead analysis the advantages in time
and space efficiency in using the IOO method. The IOO method is designed to adapt to
system dynamism to yield suboptimal performance. In cloud experiments on IBM RC2
cloud, we execute 20,000 tasks in LIGO (Laser Interferometer Gravitational-wave
Observatory) verification workflow on 128 virtual machines.
The IOO schedule is generated in less than 1,000 seconds, while using the Monte
Carlo simulation takes 27.6 hours, 100 times longer to yield an optimal schedule. The
IOO-optimized schedule results in a throughput of 1,100 tasks/sec with 7 GB memory
demand, compared with 60 percent decrease in throughput and 70 percent increase in
memory demand in using the Monte Carlo method. Our LIGO experimental results clearly
demonstrate the advantage of using the IOO-based workflow scheduling over the
traditional blind-pick, ordinal optimization, or Monte Carlo methods. These numerical
results are also validated by the theoretical complexity and overhead analysis provided.
27. A Secure Cloud Computing Based Framework for Big Data Information
management of Smart Grid
Abstract:—
Smart grid is a technological innovation that improves efficiency, reliability,
economics, and sustainability of electricity services. It plays a crucial role in modern energy
infrastructure. The main challenges of smart grids, however, are how to manage different
types of front-end intelligent devices such as power assets and smart meters efficiently; and
how to process a huge amount of data received from these devices. Cloud computing, a
technology that provides computational resources on demands, is a good candidate to address
Contact: 8681073727, ehivetechnologies@gmail.com
these challenges since it has several good properties such as energy saving, cost saving,
agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based
framework for big data information management in smart grids, which we call “SmartFrame.”
The main idea of our framework is to build a hierarchical structure of cloud
computing centers to provide different types of computing services for information
management and big data analysis. In addition to this structural framework, we present a
security solution based on identity-based encryption, signature and proxy re-encryption to
address critical security issues of the proposed framework.
28. An Authenticated Trust and Reputation Calculation and Management System for
Cloud and Sensor Networks Integration
Abstract:—
Induced by incorporating the powerful data storage and data processing abilities of
cloud computing (CC) as well as ubiquitous data gathering capability of wireless sensor
networks (WSNs), CC-WSN integration received a lot of attention from both academia and
industry. However, authentication as well as trust and reputation calculation and management
of cloud service providers (CSPs) and sensor network providers (SNPs) are two very critical
and barely explored issues for this new paradigm. To fill the gap, this paper proposes a novel
authenticated trust and reputation calculation and management (ATRCM) system for CCWSN integration.
Considering the authenticity of CSP and SNP, the attribute requirement of cloud
service user (CSU) and CSP, the cost, trust, and reputation of the service of CSP and SNP,
the proposed ATRCM system achieves the following three functions: 1) authenticating CSP
and SNP to avoid malicious impersonation attacks; 2) calculating and managing trust and
reputation regarding the service of CSP and SNP; and 3) helping CSU choose desirable CSP
and assisting CSP in selecting appropriate SNP. Detailed analysis and design as well as
further functionality evaluation results are presented to demonstrate the effectiveness of
ATRCM, followed with system security analysis.
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29. Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud
Applications
Abstract:—
Elastic partitioning of computations between mobile devices and cloud is an
important and challenging research topic formobile cloud computing. Existing works focus
on the single-user computation partitioning, which aims to optimize the application
completion time for one particular single user. These works assume that the cloud always has
enough resources to execute the computations immediately when they are offloaded to the
cloud. However, this assumption does not hold for large scale mobile cloud applications. In
these applications, due to the competition for cloud resources among a large number of users,
the offloaded computations may be executed with certain scheduling delay on the cloud.
Single user partitioning that does not take into account the scheduling delay on the cloud may
yield significant performance degradation. In this paper, we study, for the first time, multiuser computation partitioning problem (MCPP), which considers the partitioning of multiple
users’ computations together with the scheduling of offloaded computations on the cloud
resources.
Instead of pursuing the minimum application completion time for every single user,
we aim to achieve minimum average completion time for all the users, based on the number
of provisioned resources on the cloud. We show that MCPP is different from and more
difficult than the classical job scheduling problems. We design an offline heuristic algorithm,
namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that
SearchAdjust outperforms both the single user partitioning approaches and classical job
scheduling approaches by 10 percent on average in terms of application delay. Based on
SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in
practical systems. We validate the effectiveness of our online algorithm using real world load
traces.
30. Maximizing Energy Efficiency in Multiple Access Channels by Exploiting
Packet Dropping and Transmitter Buffering
Abstract:—
Quality of service (QoS) for a network is characterized in terms of various parameters
specifying packet delay and loss tolerance requirements for the application. The
unpredictable nature of the wireless channel demands for application of certain mechanisms
to meet the QoS requirements. Traditionally, medium access control (MAC) and network
layers perform these tasks. However, these mechanisms do not take (fading) channel
conditions into account.
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In this paper, we investigate the problem using cross layer techniques where
information flow and joint optimization of higher and physical layer is permitted.We propose
a scheduling scheme to optimize the energy consumption of a multiuser multi-access system
such that QoS constraints in terms of packet loss are fulfilled while the system is able to
maximize the advantages emerging from multiuser diversity. Specifically, this work focuses
on modeling and analyzing the effects of packet buffering capabilities of the transmitter on
the system energy for a packet loss tolerant application.
We discuss low complexity schemes which show comparable performance to the
proposed scheme. The numerical evaluation reveals useful insights about the coupling effects
of different QoS parameters on the system energy consumption and validates our analytical
results.
Contact: 8681073727, ehivetechnologies@gmail.com