2013 2014 ieee projects titles with abstract

TYPICAL SOFT TECHNOLOGIES
MOBILE COMPUTING
1. 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.

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2. DSS: Distributed SINR-Based Scheduling Algorithm for Multihop
Wireless Networks
Abstract :
The problem of developing distributed scheduling algorithms for high throughput in
multihop wireless networks has been extensively studied in recent years. The design of a
distributed low-complexity scheduling algorithm becomes even more challenging when taking
into account a physical interference model, which requires the SINR at a receiver to be checked
when making scheduling decisions. To do so, we need to check whether a transmission failure is
caused by interference due to simultaneous transmissions from distant nodes. In this paper, we
propose a scheduling algorithm under a physical interference model, which is amenable to
distributed implementation with 802.11 CSMA technologies. The proposed scheduling algorithm
is shown to achieve throughput optimality. We present two variations of the algorithm to
enhance the delay performance and to reduce the control overhead, respectively, while retaining
throughput optimality.

3. Toward Accurate Mobile Sensor Network Localization in Noisy
Environments
Abstract :
The node localization problem in mobile sensor networks has received significant attention.
Recently, particle filters adapted from robotics have produced good localization accuracies in
conventional settings. In spite of these successes, state-of-theart solutions suffer significantly
when used in challenging indoor and mobile environments characterized by a high degree of
radio signal irregularity. New solutions are needed to address these challenges. We propose a
fuzzy logic-based approach for mobile node localization in challenging environments.
Localization is formulated as a fuzzy multilateration problem. For sparse networks with few
available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based
localization scheme is implemented in a simulator and compared to state-of-the-art solutions.
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Extensive simulation results demonstrate improvements in the localization accuracy from 20 to
40 percent when the radio irregularity is high. A hardware implementation running on Epic
motes and transported by iRobot mobile hosts confirms simulation results and extends them to
the real world.

4. Adaptive Duty Cycle Control with Queue Management in Wireless Sensor
Networks
Abstract :
This paper proposes a control-based approach to the duty cycle adaptation for wireless sensor
networks. The proposed method controls the duty cycle through the queue management to
achieve high-performance under variable traffic rates. To have energy efficiency while
minimizing the delay, we design a feedback controller, which adapts the sleep time to the traffic
change dynamically by constraining the queue length at a predetermined value. In addition, we
propose an efficient synchronization scheme using an active pattern, which represents the active
time slot schedule for synchronization among sensor nodes, without affecting neighboring
schedules. Based on the control theory, we analyze the adaptation behavior of the proposed
controller and demonstrate system stability. The simulation results show that the proposed
method outperforms existing schemes by achieving more power savings while minimizing the
delay.

5. Cooperative Packet Delivery in Hybrid Wireless Mobile Networks: A
Coalitional Game Approach
Abstract :
We consider the problem of cooperative packet delivery to mobile nodes in a hybrid wireless
mobile network, where both infrastructure-based and infrastructure-less (i.e., ad hoc mode or
peer-to-peer mode) communications are used. We propose a solution based on a coalition
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formation among mobile nodes to cooperatively deliver packets among these mobile nodes in the
same coalition. A coalitional game is developed to analyze the behavior of the rational mobile
nodes for cooperative packet delivery. A group of mobile nodes makes a decision to join or to
leave a coalition based on their individual payoffs. The individual payoff of each mobile node is
a function of the average delivery delay for packets transmitted to the mobile node from a base
station and the cost incurred by this mobile node for relaying packets to other mobile nodes. To
find the payoff of each mobile node, a Markov chain model is formulated and the expected cost
and packet delivery delay are obtained when the mobile node is in a coalition. Since both the
expected cost and packet delivery delay depend on the probability that each mobile node will
help other mobile nodes in the same coalition to forward packets to the destination mobile node
in the same coalition, a bargaining game is used to find the optimal helping probabilities. After
the payoff of each mobile node is obtained, we find the solutions of the coalitional game which
are the stable coalitions. A distributed algorithm is presented to obtain the stable coalitions and a
Markov-chain-based analysis is used to evaluate the stable coalitional structures obtained from
the distributed algorithm. Performance evaluation results show that when the stable coalitions are
formed, the mobile nodes achieve a nonzero payoff (i.e., utility is higher than the cost). With a
coalition formation, the mobile nodes achieve higher payoff than that when each mobile node
acts alone.

6. VAPR: Void-Aware Pressure Routing for Underwater Sensor Networks
Abstract :
Underwater mobile sensor networks have recently been proposed as a way to explore and
observe the ocean, providing 4D (space and time) monitoring of underwater environments. We
consider a specialized geographic routing problem called pressure routing that directs a packet to
any sonobuoy on the surface based on depth information available from on-board pressure
gauges. The main challenge of pressure routing in sparse underwater networks has been the
efficient handling of 3D voids. In this respect, it was recently proven that the greedy stateless
perimeter routing method, very popular in 2D networks, cannot be extended to void recovery in
3D networks. Available heuristics for 3D void recovery require expensive flooding. In this paper,
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we propose a Void-Aware Pressure Routing (VAPR) protocol that uses sequence number, hop
count and depth information embedded in periodic beacons to set up nexthop direction and to
build a directional trail to the closest sonobuoy. Using this trail, opportunistic directional
forwarding can be efficiently performed even in the presence of voids. The contribution of this
paper is twofold: 1) a robust soft-state routing protocol that supports opportunistic directional
forwarding; and 2) a new framework to attain loop freedom in static and mobile underwater
networks to guarantee packet delivery. Extensive simulation results show that VAPR
outperforms existing solutions.

7. DCIM: Distributed Cache Invalidation Method for Maintaining Cache
Consistency in Wireless Mobile Networks
Abstract :
This paper proposes distributed cache invalidation mechanism (DCIM), a client-based cache
consistency scheme that is implemented on top of a previously proposed architecture for caching
data items in mobile ad hoc networks (MANETs), namely COACS, where special nodes cache
the queries and the addresses of the nodes that store the responses to these queries. We have also
previously proposed a server-based consistency scheme, named SSUM, whereas in this paper,
we introduce DCIM that is totally client-based. DCIM is a pull-based algorithm that implements
adaptive time to live (TTL), piggybacking, and prefetching, and provides near strong consistency
capabilities. Cached data items are assigned adaptive TTL values that correspond to their update
rates at the data source, where items with expired TTL values are grouped in validation requests
to the data source to refresh them, whereas unexpired ones but with high request rates are
prefetched from the server. In this paper, DCIM is analyzed to assess the delay and bandwidth
gains (or costs) when compared to polling every time and push-based schemes. DCIM was also
implemented using ns2, and compared against client-based and server-based schemes to assess
its performance experimentally. The consistency ratio, delay, and overhead traffic are reported
versus several variables, where DCIM showed to be superior when compared to the other
systems.
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8. 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.

9. Scheduling Partition for Order Optimal Capacity in Large-Scale Wireless
Networks
Abstract :
The capacity scaling property specifies the change of network throughput when network size
increases. It serves as an essential performance metric in large-scale wireless networks. Existing
results have been obtained based on the assumption of using a globally planned link transmission
schedule in the network, which is however not feasible in large wireless networks due to the
scheduling complexity. The gap between the well-known capacity results and the infeasible
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assumption on link scheduling potentially undermines our understanding of the achievable
network capacity. In this paper, we propose the scheduling partition methodology that
decomposes a large network into small autonomous scheduling zones and implements a localized
scheduling algorithm independently in each partition. We prove the sufficient and the necessary
conditions for the scheduling partition approach to achieve the same order of capacity as the
widely assumed global scheduling strategy. In comparison to the network dimension ffiffiffi n p ,
scheduling partition size

ðrðnÞÞ is sufficient to obtain the optimal capacity scaling, where rðnÞ

is the node transmission radius and much smaller than ffiffiffi n p . We finally propose a
distributed partition protocol and a localized scheduling algorithm as our scheduling solution for
maximum capacity in large wireless networks.

10.Video On-Demand Streaming in Cognitive Wireless Mesh Networks
Abstract :
Cognitive radio (CR), which enables dynamic access of underutilized licensed spectrums, is a
promising technology for more efficient spectrum utilization. Since cognitive radio enables the
access of larger amount of spectrum, it can be used to build wireless mesh networks with higher
network capacity, and thus provide better quality of services for high bit-rate applications. In this
paper, we study the multisource video on-demand application in multi-interface cognitive
wireless mesh networks. Given a video request, we find a joint multipath routing and spectrum
allocation for the session to minimize its total bandwidth cost in the network, and therefore
maximize the number of sessions the network can support. We propose both distributed and
centralized routing and channel allocation algorithms to solve the problem. Simulation results
show that our algorithms increase the maximum number of concurrent sessions that can be
supported in the network, and also improve each session’s performance with regard to spectrum
mobility.

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11.Relay Selection for Geographical Forwarding in Sleep-Wake Cycling
Wireless Sensor Networks
Abstract :
Our work is motivated by geographical forwarding of sporadic alarm packets to a base
station in a wireless sensor network (WSN), where the nodes are sleep-wake cycling periodically
and asynchronously. We seek to develop local forwarding algorithms that can be tuned so as to
tradeoff the end-to-end delay against a total cost, such as the hop count or total energy. Our
approach is to solve, at each forwarding node enroute to the sink, the local forwarding problem
of minimizing one-hop waiting delay subject to a lower bound constraint on a suitable reward
offered by the next-hop relay; the constraint serves to tune the tradeoff. The reward metric used
for the local problem is based on the end-to-end total cost objective (for instance, when the total
cost is hop count, we choose to use the progress toward sink made by a relay as the reward). The
forwarding node, to begin with, is uncertain about the number of relays, their wake-up times, and
the reward values, but knows the probability distributions of these quantities. At each relay
wake-up instant, when a relay reveals its reward value, the forwarding node’s problem is to
forward the packet or to wait for further relays to wake-up. In terms of the operations research
literature, our work can be considered as a variant of the asset selling problem. We formulate our
local forwarding problem as a partially observable Markov decision process (POMDP) and
obtain inner and outer bounds for the optimal policy. Motivated by the computational complexity
involved in the policies derived out of these bounds, we formulate an alternate simplified model,
the optimal policy for which is a simple threshold rule. We provide simulation results to compare
the performance of the inner and outer bound policies against the simple policy, and also against
the optimal policy when the source knows the exact number of relays. Observing the good
performance and the ease of implementation of the simple policy, we apply it to our motivating
problem, i.e., local geographical routing of sporadic alarm packets in a large WSN. We compare
the end-to-end performance (i.e., average total delay and average total cost) obtained by the
simple policy, when used for local geographical forwarding, against that obtained by the globally
optimal forwarding algorithm proposed by Kim.

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12.Adaptive Position Update for Geographic Routing in Mobile Ad Hoc
Networks
Abstract :
In geographic routing, nodes need to maintain up-to-date positions of their immediate
neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets
that contain the geographic location coordinates of the nodes is a popular method used by most
geographic routing protocols to maintain neighbor positions. We contend and demonstrate that
periodic beaconing regardless of the node mobility and traffic patterns in the network is not
attractive from both update cost and routing performance points of view. We propose the
Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the
frequency of position updates based on the mobility dynamics of the nodes and the forwarding
patterns in the network. APU is based on two simple principles: 1) nodes whose movements are
harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to
forwarding paths update their positions more frequently (and vice versa). Our theoretical
analysis, which is validated by NS2 simulations of a well-known geographic routing protocol,
Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce
the update cost and improve the routing performance in terms of packet delivery ratio and
average end-to-end delay in comparison with periodic beaconing and other recently proposed
updating schemes. The benefits of APU are further confirmed by undertaking evaluations in
realistic network scenarios, which account for localization error, realistic radio propagation, and
sparse network.

13.Channel Allocation and Routing in Hybrid Multichannel Multiradio
Wireless Mesh Networks
Abstract :

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Many efforts have been devoted to maximizing network throughput in a multichannel
multiradio wireless mesh network. Most current solutions are based on either purely static or
purely dynamic channel allocation approaches. In this paper, we propose a hybrid multichannel
multiradio wireless mesh networking architecture, where each mesh node has both static and
dynamic interfaces. We first present an Adaptive Dynamic Channel Allocation protocol
(ADCA), which considers optimization for both throughput and delay in the channel assignment.
In addition, we also propose an Interference and Congestion Aware Routing protocol (ICAR) in
the hybrid network with both static and dynamic links, which balances the channel usage in the
network. Our simulation results show that compared to previous works, ADCA reduces the
packet delay considerably without degrading the network throughput. The hybrid architecture
shows much better adaptivity to changing traffic than purely static architecture without dramatic
increase in overhead, and achieves lower delay than existing approaches for hybrid networks.

14.Toward Privacy Preserving and Collusion Resistance in a Location Proof
Updating System
Abstract :
Today’s location-sensitive service relies on user’s mobile device to determine the current
location. This allows malicious users to access a restricted resource or provide bogus alibis by
cheating on their locations. To address this issue, we propose A Privacy-Preserving LocAtion
proof Updating System (APPLAUS) in which colocated Bluetooth enabled mobile devices
mutually generate location proofs and send updates to a location proof server. Periodically
changed pseudonyms are used by the mobile devices to protect source location privacy from
each other, and from the untrusted location proof server. We also develop user-centric location
privacy model in which individual users evaluate their location privacy levels and decide
whether and when to accept the location proof requests. In order to defend against colluding
attacks, we also present betweenness ranking-based and correlation clustering-based approaches
for outlier detection. APPLAUS can be implemented with existing network infrastructure, and
can be easily deployed in Bluetooth enabled mobile devices with little computation or power
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cost. Extensive experimental results show that APPLAUS can effectively provide location
proofs, significantly preserve the source location privacy, and effectively detect colluding
attacks.

15.SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’
Heterogeneity
Abstract :
Fingerprint-based methods are widely adopted for indoor localization purpose because of
their cost-effectiveness compared to other infrastructure-based positioning systems. However,
the popular location fingerprint, Received Signal Strength (RSS), is observed to differ
significantly across different devices’ hardware even under the same wireless conditions. We
derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD),
and verify its performance experimentally using a number of different mobile devices with
heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth
devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted
localization. We present the results of two well-known localization algorithms (K Nearest
Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its
robustness when the testing device differs from the training device. We also compare these SSDbased localization algorithms’ performance against that of two other approaches in the literature
that are designed to mitigate the effects of mobile node hardware variations, and show that SSDbased algorithms have better accuracy.

16.EMAP: Expedite Message Authentication Protocol for Vehicular Ad Hoc
Networks
Abstract :
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Vehicular ad hoc networks (VANETs) adopt the Public Key Infrastructure (PKI) and
Certificate Revocation Lists (CRLs) for their security. In any PKI system, the authentication of a
received message is performed by checking if the certificate of the sender is included in the
current CRL, and verifying the authenticity of the certificate and signature of the sender. In this
paper, we propose an Expedite Message Authentication Protocol (EMAP) for VANETs, which
replaces the time-consuming CRL checking process by an efficient revocation checking process.
The revocation check process in EMAP uses a keyed Hash Message Authentication Code
ðHMACÞ, where the key used in calculating theHMAC is shared only between nonrevoked OnBoard Units (OBUs). In addition, EMAP uses a novel probabilistic key distribution, which
enables nonrevoked OBUs to securely share and update a secret key. EMAP can significantly
decrease the message loss ratio due to the message verification delay compared with the
conventional authentication methods employing CRL. By conducting security analysis and
performance evaluation,EMAP is demonstrated to be secure and efficient.

17.Channel Assignment for Throughput Optimization in Multichannel
Multiradio Wireless Mesh Networks Using Network Coding
Abstract :
Compared to single-hop networks such as WiFi, multihop infrastructure wireless mesh
networks (WMNs) can potentially embrace the broadcast benefits of a wireless medium in a
more flexible manner. Rather than being point-to-point, links in the WMNs may originate from a
single node and reach more than one other node. Nodes located farther than a one-hop distance
and overhearing such transmissions may opportunistically help relay packets for previous hops.
This phenomenon is called opportunistic overhearing/ listening. With multiple radios, a node can
also improve its capacity by transmitting over multiple radios simultaneously using orthogonal
channels. Capitalizing on these potential advantages requires effective routing and efficient
mapping of channels to radios (channel assignment (CA)). While efficient channel assignment
can greatly reduce interference from nearby transmitters, effective routing can potentially relieve
congestion on paths to the infrastructure. Routing, however, requires that only packets pertaining
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to a particular connection be routed on a predetermined route. Random network coding (RNC)
breaks this constraint by allowing nodes to randomly mix packets overheard so far before
forwarding. A relay node thus only needs to know how many packets, and not which packets, it
should send. We mathematically formulate the joint problem of random network coding, channel
assignment, and broadcast link scheduling, taking into account opportunistic overhearing, the
interference constraints, the coding constraints, the number of orthogonal channels, the number
of radios per node, and fairness among unicast connections. Based on this formulation, we
develop a suboptimal, auction-based solution for overall network throughput optimization.
Performance evaluation results show that our algorithm can effectively exploit multiple radios
and channels and can cope with fairness issues arising from auctions. Our algorithm also shows
promising gains over traditional routing solutions in which various channel assignment strategies
are used.

18.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
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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 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.

19.Discovery and Verification of Neighbor Positions in Mobile Ad Hoc
Networks
Abstract :
A growing number of ad hoc networking protocols and location-aware services require that
mobile nodes learn the position of their neighbors. However, such a process can be easily abused
or disrupted by adversarial nodes. In absence of a priori trusted nodes, the discovery and
verification of neighbor positions presents challenges that have been scarcely investigated in the
literature. In this paper, we address this open issue by proposing a fully distributed cooperative
solution that is robust against independent and colluding adversaries, and can be impaired only
by an overwhelming presence of adversaries. Results show that our protocol can thwart more
than 99 percent of the attacks under the best possible conditions for the adversaries, with
minimal false positive rates.

20.Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks
Abstract :
Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such
as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge
faced by data-intensive WSNs is to transmit all the data generated within an application’s
lifetime to the base station despite the fact that sensor nodes have limited power supplies. We
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intensive WSNs. Our approach differs from previous work in two main aspects. First, it does not
require complex motion planning of mobile nodes, so it can be implemented on a number of lowcost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility
and wireless transmissions into a holistic optimization framework. Our framework consists of
three main algorithms. The first algorithm computes an optimal routing tree assuming no nodes
can move. The second algorithm improves the topology of the routing tree by greedily adding
new nodes exploiting mobility of the newly added nodes. The third algorithm improves the
routing tree by relocating its nodes without changing its topology. This iterative algorithm
converges on the optimal position for each node given the constraint that the routing tree
topology does not change. We present efficient distributed implementations for each algorithm
that require only limited, localized synchronization. Because we do not necessarily compute an
optimal topology, our final routing tree is not necessarily optimal. However, our simulation
results show that our algorithms significantly outperform the best existing solutions.

21.Vampire Attacks: Draining Life from Wireless Ad Hoc Sensor Networks
Abstract :
Ad hoc low-power wireless networks are an exciting research direction in sensing and
pervasive computing. Prior security work in this area has focused primarily on denial of
communication at the routing or medium access control levels. This paper explores resource
depletion attacks at the routing protocol layer, which permanently disable networks by quickly
draining nodes’ battery power. These ―Vampire‖ attacks are not specific to any specific protocol,
but rather rely on the properties of many popular classes of routing protocols. We find that all
examined protocols are susceptible to Vampire attacks, which are devastating, difficult to detect,
and are easy to carry out using as few as one malicious insider sending only protocol-compliant
messages. In the worst case, a single Vampire can increase network-wide energy usage by a
factor of OðNÞ, where N in the number of network nodes. We discuss methods to mitigate these
types of attacks, including a new proof-of-concept protocol that provably bounds the damage
caused by Vampires during the packet forwarding phase.

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CLOUD COMPUTING
1. 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 servicelevel agreement, the satisfaction of a consumer, the quality of a service, the penalty of a lowquality 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 constantspeed 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.

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2. 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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3. Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of
Linear Equations
Abstract :

Cloud computing economically enables customers with limited computational resources to
outsource large-scale computations to the cloud. However, how to protect customers’
confidential data involved in the computations then becomes a major security concern. In this
paper, we present a secure outsourcing mechanism for solving large-scale systems of linear
equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or
LU decomposition (aka. direct method) to such large-scale LEs would be prohibitively
expensive, we build the secure LE outsourcing mechanism via a completely different approach—
iterative method, which is much easier to implement in practice and only demands relatively
simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely
harness the cloud for iteratively finding successive approximations to the LE solution, while
keeping both the sensitive input and output of the computation private. For robust cheating
detection, we further explore the algebraic property of matrix-vector operations and propose an
efficient result verification mechanism, which allows the customer to verify all answers received
from previous iterative approximations in one batch with high probability. Thorough security
analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of
our proposed design.

4. QoS Ranking Prediction for Cloud Services
Abstract :

Cloud computing is becoming popular. Building high-quality cloud applications is a critical
research problem. QoS rankings provide valuable information for making optimal cloud service
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world invocations on the service candidates are usually required. To avoid the time-consuming
and expensive real-world service invocations, this paper proposes a QoS ranking prediction
framework for cloud services by taking advantage of the past service usage experiences of other
consumers. Our proposed framework requires no additional invocations of cloud services when
making QoS ranking prediction. Two personalized QoS ranking prediction approaches are
proposed to predict the QoS rankings directly. Comprehensive experiments are conducted
employing real-world QoS data, including 300 distributed users and 500 realworld web services
all over the world. The experimental results show that our approaches outperform other
competing approaches.

5. Cloudy with a Chance of Cost Savings
Abstract :

Cloud-based hosting is claimed to possess many advantages over traditional in-house (onpremise) hosting such as better scalability, ease of management, and cost savings. It is not
difficult to understand how cloud-based hosting can be used to address some of the existing
limitations and extend the capabilities of many types of applications. However, one of the most
important questions is whether cloud-based hosting will be economically feasible for my
application if migrated into the cloud. It is not straightforward to answer this question because it
is not clear how my application will benefit from the claimed advantages, and, in turn, be able to
convert them into tangible cost savings. Within cloud-based hosting offerings, there is a wide
range of hosting options one can choose from, each impacting the cost in a different way.
Answering these questions requires an in-depth understanding of the cost implications of all the
possible choices specific to my circumstances. In this study, we identify a diverse set of key
factors affecting the costs of deployment choices. Using benchmarks representing two different
applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment
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choices. We consider important application characteristics such as workload intensity, growth
rate, traffic size, storage, and software license to understand their impact on the overall costs. We
also discuss the impact of workload variance and cloud elasticity, and certain cost factors that are
subjective in nature.

6. Error-Tolerant Resource Allocation and Payment Minimization for Cloud
System
Abstract :

With virtual machine (VM) technology being increasingly mature, compute resources in
cloud systems can be partitioned in fine granularity and allocated on demand. We make three
contributions in this paper: 1) We formulate a deadline-driven resource allocation problem based
on the cloud environment facilitated with VM resource isolation technology, and also propose a
novel solution with polynomial time, which could minimize users’ payment in terms of their
expected deadlines. 2) By analyzing the upper bound of task execution length based on the
possibly inaccurate workload prediction, we further propose an error-tolerant method to
guarantee task’s completion within its deadline. 3) We validate its effectiveness over a real VMfacilitated cluster environment under different levels of competition. In our experiment, by
tuning algorithmic input deadline based on our derived bound, task execution length can always
be limited within its deadline in the sufficient-supply situation; the mean execution length still
keeps 70 percent as high as userspecified deadline under the severe competition. Under the
original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as
high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20
percent of tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of
deadlines.

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7. Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the
Cloud
Abstract :

With the character of low maintenance, cloud computing provides an economical and
efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a
multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a
challenging issue, due to the frequent change of the membership. In this paper, we propose a
secure multiowner data sharing scheme, named Mona, for dynamic groups in the cloud. By
leveraging group signature and dynamic broadcast encryption techniques, any cloud user can
anonymously share data with others. Meanwhile, the storage overhead and encryption
computation cost of our scheme are independent with the number of revoked users. In addition,
we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of
our scheme in experiments.

8. A New Disk I/O Model of Virtualized Cloud Environment
Abstract :

In a traditional virtualized cloud environment, using asynchronous I/O in the guest file
system and synchronous I/O in the host file system to handle an asynchronous user disk write
exhibits several drawbacks, such as performance disturbance among different guests and
consistency maintenance across guest failures. To improve these issues, this paper introduces a
novel disk I/O model for virtualized cloud system called HypeGear, where the guest file system
uses synchronous operations to deal with the guest write request and the host file system
performs asynchronous operations to write the data to the hard disk. A prototype system is
implemented on the Xen hypervisor and our experimental results verify that this new model has
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many advantages over the conventional asynchronous-synchronous model. We also evaluate the
overhead of asynchronous I/O at host, which is brought by our new model. The result
demonstrates that it enforces little cost on host layer.

9. On Data Staging Algorithms for Shared Data Accesses in Clouds
Abstract :

In this paper, we study the strategies for efficiently achieving data staging and caching on a
set of vantage sites in a cloud system with a minimum cost. Unlike the traditional research, we
do not intend to identify the access patterns to facilitate the future requests. Instead, with such a
kind of information presumably known in advance, our goal is to efficiently stage the shared data
items to predetermined sites at advocated time instants to align with the patterns while
minimizing the monetary costs for caching and transmitting the requested data items. To this
end, we follow the cost and network models in [1] and extend the analysis to multiple data items,
each with single or multiple copies. Our results show that under homogeneous cost model, when
the ratio of transmission cost and caching cost is low, a single copy of each data item can
efficiently serve all the user requests. While in multicopy situation, we also consider the tradeoff
between the transmission cost and caching cost by controlling the upper bounds of transmissions
and copies. The upper bound can be given either on per-item basis or on all-item basis. We
present efficient optimal solutions based on dynamic programming techniques to all these cases
provided that the upper bound is polynomially bounded by the number of service requests and
the number of distinct data items. In addition to the homogeneous cost model, we also briefly
discuss this problem under a heterogeneous cost model with some simple yet practical
restrictions and present a 2-approximation algorithm to the general case. We validate our
findings by implementing a data staging solver, whereby conducting extensive simulation studies
on the behaviors of the algorithms.

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10.Dynamic Optimization of Multiattribute Resource Allocation in SelfOrganizing Clouds
Abstract :

By leveraging virtual machine (VM) technology which provides performance and fault isolation,
cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than
in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a
gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped
commodity computing power over the Internet. Toward this new architecture where each
participant may autonomously act as both resource consumer and provider, we propose a fully
distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our
approach not only achieves maximized resource utilization using the proportional share model
(PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a
novel multiattribute range query protocol for locating qualified nodes. Contrary to existing
solutions which often generate bulky messages per request, our protocol produces only one
lightweight query message per task on the Content Addressable Network (CAN). It works
effectively to find for each task its qualified resources under a randomized policy that mitigates
the contention among requesters. We show the SOC with our optimized algorithms can make an
improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also
exhibits fairly high adaptability in a dynamic node-churning environment.

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11.Scalable and Secure Sharing of Personal Health Records in Cloud
Computing Using Attribute-Based Encryption
Abstract :

Personal health record (PHR) is an emerging patient-centric model of health information
exchange, which is often outsourced to be stored at a third party, such as cloud providers.
However, there have been wide privacy concerns as personal health information could be
exposed to those third party servers and to unauthorized parties. To assure the patients’ control
over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing.
Yet, issues such as risks of privacy exposure, scalability in key management, flexible access, and
efficient user revocation, have remained the most important challenges toward achieving finegrained, cryptographically enforced data access control. In this paper, we propose a novel
patient-centric framework and a suite of mechanisms for data access control to PHRs stored in
semitrusted servers. To achieve fine-grained and scalable data access control for PHRs, we
leverage attribute-based encryption (ABE) techniques to encrypt each patient’s PHR file.
Different from previous works in secure data outsourcing, we focus on the multiple data owner
scenario, and divide the users in the PHR system into multiple security domains that greatly
reduces the key management complexity for owners and users. A high degree of patient privacy
is guaranteed simultaneously by exploiting multiauthority ABE. Our scheme also enables
dynamic modification of access policies or file attributes, supports efficient on-demand
user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical
and experimental results are presented which show the security, scalability, and efficiency of our
proposed scheme.

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PARALLEL AND DISTRIBUTED SYSTEMS
1. A Truthful Dynamic Workflow Scheduling Mechanism for Commercial
Multicloud Environments
Abstract :
The ultimate goal of cloud providers by providing resources is increasing their revenues. This
goal leads to a selfish behavior that negatively affects the users of a commercial multicloud
environment. In this paper, we introduce a pricing model and a truthful mechanism for
scheduling single tasks considering two objectives: monetary cost and completion time. With
respect to the social cost of the mechanism, i.e., minimizing the completion time and monetary
cost, we extend the mechanism for dynamic scheduling of scientific workflows. We theoretically
analyze the truthfulness and the efficiency of the mechanism and present extensive experimental
results showing significant impact of the selfish behavior of the cloud providers on the efficiency
of the whole system. The experiments conducted using real-world and synthetic workflow
applications demonstrate that our solutions dominate in most cases the Pareto-optimal solutions
estimated by two classical multiobjective evolutionary algorithms.

2. 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
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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.

3. 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-asyou-go model, storage strategies and benchmarking approaches have been developed for costeffectively 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 costeffective
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 costeffectiveness 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.

4. Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for
Production Cloud Computing Systems
Abstract :

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Performance diagnosis is labor intensive in production cloud computing systems. Such
systems typically face many realworld challenges, which the existing diagnosis techniques for
such distributed systems cannot effectively solve. An efficient, unsupervised diagnosis tool for
locating fine-grained performance anomalies is still lacking in production cloud computing
systems. This paper proposes CloudDiag to bridge this gap. Combining a statistical technique
and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of
the performance problems, which does not require any domain-specific knowledge to the target
system. CloudDiag has been applied in a practical production cloud computing systems to
diagnose performance problems. We demonstrate the effectiveness of CloudDiag in three realworld case studies.

5. Scalable and Accurate Graph Clustering and Community Structure
Detection
Abstract :
One of the most useful measures of cluster quality is the modularity of the partition, which
measures the difference between the number of the edges joining vertices from the same cluster
and the expected number of such edges in a random graph. In this paper, we show that the
problem of finding a partition maximizing the modularity of a given graph G can be reduced to a
minimum weighted cut (MWC) problem on a complete graph with the same vertices as G. We
then show that the resulting minimum cut problem can be efficiently solved by adapting existing
graph partitioning techniques. Our algorithm finds clusterings of a comparable quality and is
much faster than the existing clustering algorithms.

6. 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
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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 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.

7. SPOC: A Secure and Privacy-Preserving Opportunistic Computing
Framework for Mobile-Healthcare Emergency
Abstract :
With the pervasiveness of smart phones and the advance of wireless body sensor networks
(BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider
into a pervasive environment for better health monitoring, has attracted considerable interest
recently. However, the flourish of m-Healthcare still faces many challenges including
information security and privacy preservation. In this paper, we propose a secure and privacypreserving opportunistic computing framework, called SPOC, for m-Healthcare emergency.
With SPOC, smart phone resources including computing power and energy can be
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opportunistically gathered to process the computing-intensive personal health information (PHI)
during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the
PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare
emergency, we introduce an efficient user-centric privacy access control in SPOC framework,
which is based on an attribute-based access control and a new privacy-preserving scalar product
computation (PPSPC) technique, and allows a medical user to decide who can participate in the
opportunistic computing to assist in processing his overwhelming PHI data. Detailed security
analysis shows that the proposed SPOC framework can efficiently achieve user-centric privacy
access control in m- Healthcare emergency. In addition, performance evaluations via extensive
simulations demonstrate the SPOC’s effectiveness in term of providing high-reliable-PHI
process and transmission while minimizing the privacy disclosure during m-Healthcare
emergency.

8. Improve Efficiency and Reliability in Single-Hop WSNs with TransmitOnly Nodes
Abstract :
Wireless Sensor Networks (WSNs) will play a significant role at the ―edge‖ of the future
―Internet of Things.‖ In particular, WSNs with transmit-only nodes are attracting more attention
due to their advantages in supporting applications requiring dense and long-lasting deployment at
a very low cost and energy consumption. However, the lack of receivers in transmit-only nodes
renders most existing MAC protocols invalid. Based on our previous study on WSNs with pure
transmit-only nodes, this work proposes a simple, yet cost effective and powerful single-hop
hybrid WSN cluster architecture that contains not only transmit-only nodes but also standard
nodes (with transceivers). Along with the hybrid architecture, this work also proposes a new
MAC layer protocol framework called Robust Asynchronous Resource Estimation (RARE) that
efficiently and reliably manages the densely deployed single-hop hybrid cluster in a selforganized fashion. Through analysis and extensive simulations, the proposed framework is
shown to meet or exceed the needs of most applications in terms of the data delivery probability,
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QoS differentiation, system capacity, energy consumption, and reliability. To the best of our
knowledge, this work is the first that brings reliable scheduling to WSNs containing both
nonsynchronized transmit-only nodes and standard nodes.

9. Optimal Client-Server Assignment for Internet Distributed Systems
Abstract :
We investigate an underlying mathematical model and algorithms for optimizing the
performance of a class of distributed systems over the Internet. Such a system consists of a large
number of clients who communicate with each other indirectly via a number of intermediate
servers. Optimizing the overall performance of such a system then can be formulated as a clientserver assignment problem whose aim is to assign the clients to the servers in such a way to
satisfy some prespecified requirements on the communication cost and load balancing. We show
that 1) the total communication load and load balancing are two opposing metrics, and
consequently, their tradeoff is inherent in this class of distributed systems; 2) in general, finding
the optimal client-server assignment for some prespecified requirements on the total load and
load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex
optimization for finding the approximate solution. Our simulation results indicate that the
proposed algorithm produces superior performance than other heuristics, including the popular
Normalized Cuts algorithm.

10.Fast Channel Zapping with Destination-Oriented Multicast for IP Video
Delivery
Abstract :
Channel zapping time is a critical quality of experience (QoE) metric for IP-based video
delivery systems such as IPTV. An interesting zapping acceleration scheme based on timeshifted subchannels (TSS) was recently proposed, which can ensure a zapping delay bound as
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well as maintain the picture quality during zapping. However, the behaviors of the TSS-based
scheme have not been fully studied yet. Furthermore, the existing TSS-based implementation
adopts the traditional IP multicast, which is not scalable for a large-scale distributed system.
Corresponding to such issues, this paper makes contributions in two aspects. First, we resort to
theoretical analysis to understand the fundamental properties of the TSS-based service model.
We show that there exists an optimal subchannel data rate which minimizes the redundant traffic
transmitted over subchannels. Moreover, we reveal a start-up effect, where the existing operation
pattern in the TSS-based model could violate the zapping delay bound. With a solution proposed
to resolve the start-up effect, we rigorously prove that a zapping delay bound equal to the
subchannel time shift is guaranteed by the updated TSS-based model. Second, we propose a
destination-oriented-multicast (DOM) assisted zapping acceleration (DAZA) scheme for a
scalable TSS-based implementation, where a subscriber can seamlessly migrate from a
subchannel to the main channel after zapping without any control message exchange over the
network. Moreover, the subchannel selection in DAZA is independent of the zapping request
signaling delay, resulting in improved robustness and reduced messaging overhead in a
distributed environment. We implement DAZA in ns-2 and multicast an MPEG-4 video stream
over a practical network topology. Extensive simulation results are presented to demonstrate the
validity of our analysis and DAZA scheme.

11.Cluster-Based Certificate Revocation with Vindication Capability for
Mobile Ad Hoc Networks
Abstract :
Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and
ease of deployment. However, the wireless and dynamic natures render them more vulnerable to
various types of security attacks than the wired networks. The major challenge is to guarantee
secure network services. To meet this challenge, certificate revocation is an important integral
component to secure network communications. In this paper, we focus on the issue of certificate
revocation to isolate attackers from further participating in network activities. For quick and
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accurate certificate revocation, we propose the Cluster-based Certificate Revocation with
Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme,
we recover the warned nodes to take part in the certificate revocation process; to enhance the
accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as
legitimate nodes or not, before recovering them. The performances of our scheme are evaluated
by both numerical and simulation analysis. Extensive results demonstrate that the proposed
certificate revocation scheme is effective and efficient to guarantee secure communications in
mobile ad hoc networks.

12.A Secure Protocol for Spontaneous Wireless Ad Hoc Networks Creation
Abstract :
This paper presents a secure protocol for spontaneous wireless ad hoc networks which uses
an hybrid symmetric/ asymmetric scheme and the trust between users in order to exchange the
initial data and to exchange the secret keys that will be used to encrypt the data. Trust is based on
the first visual contact between users. Our proposal is a complete self-configured secure protocol
that is able to create the network and share secure services without any infrastructure. The
network allows sharing resources and offering new services among users in a secure
environment. The protocol includes all functions needed to operate without any external support.
We have designed and developed it in devices with limited resources. Network creation stages
are detailed and the communication, protocol messages, and network management are explained.
Our proposal has been implemented in order to test the protocol procedure and performance.
Finally, we compare the protocol with other spontaneous ad hoc network protocols in order to
highlight its features and we provide a security analysis of the system.

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13.Dynamic Resource Allocation Using Virtual Machines for Cloud
Computing Environment
Abstract :
Cloud computing allows business customers to scale up and down their resource usage based
on needs. Many of the touted gains in the cloud model come from resource multiplexing through
virtualization technology. In this paper, we present a system that uses virtualization technology
to allocate data center resources dynamically based on application demands and support green
computing by optimizing the number of servers in use. We introduce the concept of ―skewness‖
to measure the unevenness in the multidimensional resource utilization of a server. By
minimizing skewness, we can combine different types of workloads nicely and improve the
overall utilization of server resources. We develop a set of heuristics that prevent overload in the
system effectively while saving energy used. Trace driven simulation and experiment results
demonstrate that our algorithm achieves good performance.

14.High Performance Resource Allocation Strategies for Computational
Economies
Abstract :
Utility computing models have long been the focus of academic research, and with the recent
success of commercial cloud providers, computation and storage is finally being realized as the
fifth utility. Computational economies are often proposed as an efficient means of resource
allocation, however adoption has been limited due to a lack of performance and high overheads.
In this paper, we address the performance limitations of existing economic allocation models by
defining strategies to reduce the failure and reallocation rate, increase occupancy and thereby
increase the obtainable utilization of the system. The high-performance resource utilization
strategies presented can be used by market participants without requiring dramatic changes to the
allocation protocol. The strategies considered include overbooking, advanced reservation, justin-time bidding, and using substitute providers for service delivery. The proposed strategies have
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been implemented in a distributed metascheduler and evaluated with respect to Grid and cloud
deployments. Several diverse synthetic workloads have been used to quantity both the
performance benefits and economic implications of these strategies.

15.A Privacy Leakage Upper Bound Constraint-Based Approach for CostEffective Privacy Preserving of Intermediate Data Sets in Cloud
Abstract :
Cloud computing provides massive computation power and storage capacity which enable
users to deploy computation and data-intensive applications without infrastructure investment.
Along the processing of such applications, a large volume of intermediate data sets will be
generated, and often stored to save the cost of recomputing them. However, preserving the
privacy of intermediate data sets becomes a challenging problem because adversaries may
recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting
ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But
we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because
it is very time consuming and costly for data-intensive applications to en/decrypt data sets
frequently while performing any operation on them. In this paper, we propose a novel upper
bound privacy leakage constraint-based approach to identify which intermediate data sets need to
be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy
requirements of data holders can still be satisfied. Evaluation results demonstrate that the
privacy-preserving cost of intermediate data sets can be significantly reduced with our approach
over existing ones where all data sets are encrypted.

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16.A Secure Payment Scheme with Low Communication and Processing
Overhead for Multihop Wireless Networks
Abstract :
We propose RACE, a report-based payment scheme for multihop wireless networks to
stimulate node cooperation, regulate packet transmission, and enforce fairness. The nodes submit
lightweight payment reports (instead of receipts) to the accounting center (AC) and temporarily
store undeniable security tokens called Evidences. The reports contain the alleged charges and
rewards without security proofs, e.g., signatures. The AC can verify the payment by investigating
the consistency of the reports, and clear the payment of the fair reports with almost no processing
overhead or cryptographic operations. For cheating reports, the Evidences are requested to
identify and evict the cheating nodes that submit incorrect reports. Instead of requesting the
Evidences from all the nodes participating in the cheating reports, RACE can identify the
cheating nodes with requesting few Evidences. Moreover, Evidence aggregation technique is
used to reduce the Evidences’ storage area. Our analytical and simulation results demonstrate
that RACE requires much less communication and processing overhead than the existing receiptbased schemes with acceptable payment clearance delay and storage area. This is essential for
the effective implementation of a payment scheme because it uses micropayment and the
overhead cost should be much less than the payment value. Moreover, RACE can secure the
payment and precisely identify the cheating nodes without false accusations.

17.Mobi-Sync: Efficient Time Synchronization for Mobile Underwater Sensor
Networks
Abstract :
Time synchronization is an important requirement for many services provided by distributed
networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless
Sensor Networks (WSNs). However, none of them can be directly applied to Underwater Sensor
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Networks (UWSNs). A synchronization algorithm forUWSNs must consider additional factors
such as long propagation delays from the use of acoustic communication and sensor node
mobility. These unique challenges make the accuracy of synchronization procedures for UWSNs
even more critical. Time synchronization solutions specifically designed for UWSNs are needed
to satisfy these new requirements. This paper proposes Mobi-Sync, a novel time synchronization
scheme for mobile underwater sensor networks. Mobi-Sync distinguishes itself from previous
approaches for terrestrial WSN by considering spatial correlation among the mobility patterns of
neighboring UWSNs nodes. This enables Mobi-Sync to accurately estimate the long dynamic
propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in
both accuracy and energy efficiency.

18.Detection and Localization of Multiple Spoofing Attackers in Wireless
Networks
Abstract :
Wireless spoofing attacks are easy to launch and can significantly impact the performance of
networks. Although the identity of a node can be verified through cryptographic authentication,
conventional security approaches are not always desirable because of their overhead
requirements. In this paper, we propose to use spatial information, a physical property associated
with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting
spoofing attacks; 2) determining the number of attackers when multiple adversaries
masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to
use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to
detect the spoofing attacks. We then formulate the problem of determining the number of
attackers as a multiclass detection problem. Cluster-based mechanisms are developed to
determine the number of attackers. When the training data are available, we explore using the
Support Vector Machines (SVM) method to further improve the accuracy of determining the
number of attackers. In addition, we developed an integrated detection and localization system
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that can localize the positions of multiple attackers. We evaluated our techniques through two
testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real
office buildings. Our experimental results show that our proposed methods can achieve over 90
percent Hit Rate and Precision when determining the number of attackers. Our localization
results using a representative set of algorithms provide strong evidence of high accuracy of
localizing multiple adversaries.

KNOWLEDGE AND DATA ENGINEERING
1. Crowdsourced Trace Similarity with Smartphones
Abstract :
Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS,
accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced
computing services, which contribute to the solution of complex problems in a distributed
manner. In this work, we leverage such a computing paradigm to solve efficiently the following
problem: comparing a query trace Q against a crowd of traces generated and stored on
distributed smartphones. Our proposed framework, coined SmartTraceþ, provides an effective
solution without disclosing any part of the crowd traces to the query processor. SmartTraceþ,
relies on an in-situ data storage model and intelligent top-K query processing algorithms that
exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order
to derive the most relevant answers to Q. We evaluate our algorithms on both synthetic and real
workloads. We describe our prototype system developed on the Android OS. The solution is
deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that
computations over SmartTraceþ result in substantial energy conservation; in addition, results can
be computed faster than competitive approaches.

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2. Incentive Compatible Privacy-Preserving Data Analysis
Abstract :
In many cases, competing parties who have private data may collaboratively conduct
privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or
analysis results. Most often, the competing parties have different incentives. Although certain
PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is
impossible to verify whether participating parties are truthful about their private input data.
Unless proper incentives are set, current PPDA techniques cannot prevent participating parties
from modifying their private inputs. This raises the question of how to design incentive
compatible privacy-preserving data analysis techniques that motivate participating parties to
provide truthful inputs. In this paper, we first develop key theorems, then base on these
theorems, we analyze certain important privacy-preserving data analysis tasks that could be
conducted in a way that telling the truth is the best choice for any participating party.

3. 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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4. Failure-Aware Cascaded Suppression in Wireless Sensor Networks
Abstract :
Wireless sensor networks are widely used to continuously collect data from the environment.
Because of energy constraints on battery-powered nodes, it is critical to minimize
communication. Suppression has been proposed as a way to reduce communication by using
predictive models to suppress reporting of predictable data. However, in the presence of
communication failures, missing data are difficult to interpret because these could have been
either suppressed or lost in transmission. There is no existing solution for handling failures for
general, spatiotemporal suppression that uses cascading. While cascading further reduces
communication, it makes failure handling difficult, because nodes can act on incomplete or
incorrect information and in turn affect other nodes. We propose a cascaded suppression
framework that exploits both temporal and spatial data correlation to reduce communication, and
applies coding theory and Bayesian inference to recover missing data resulted from suppression
and communication failures. Experiment results show that cascaded suppression significantly
reduces communication cost and improves missing data recovery compared to existing
approaches.

5. Optimal Route Queries with Arbitrary Order Constraints
Abstract :
Given a set of spatial points DS, each of which is associated with categorical information,
e.g., restaurant, pub, etc., the optimal route query finds the shortest path that starts from the
query point (e.g., a home or hotel), and covers a user-specified set of categories (e.g., {pub,
restaurant, museum}). The user may also specify partial order constraints between different
categories, e.g., a restaurant must be visited before a pub. Previous work has focused on a special
case where the query contains the total order of all categories to be visited (e.g., museum !
restaurant ! pub). For the general scenario without such a total order, the only known solution
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reduces the problem to multiple, total-order optimal route queries. As we show in this paper, this
naı¨ve approach incurs a significant amount of repeated computations, and, thus, is not scalable
to large data sets. Motivated by this, we propose novel solutions to the general optimal route
query, based on two different methodologies, namely backward search and forward search. In
addition, we discuss how the proposed methods can be adapted to answer a variant of the optimal
route queries, in which the route only needs to cover a subset of the given categories. Extensive
experiments, using both real and synthetic data sets, confirm that the proposed solutions are
efficient and practical, and outperform existing methods by large margins.

6. Co-Occurrence-Based Diffusion for Expert Search on the Web
Abstract :
Expert search has been studied in different contexts, e.g., enterprises, academic communities.
We examine a general expert search problem: searching experts on the web, where millions of
webpages and thousands of names are considered. It has mainly two challenging issues: 1)
webpages could be of varying quality and full of noises; 2) The expertise evidences scattered in
webpages are usually vague and ambiguous. We propose to leverage the large amount of cooccurrence information to assess relevance and reputation of a person name for a query topic.
The co-occurrence structure is modeled using a hypergraph, on which a heat diffusion based
ranking algorithm is proposed. Query keywords are regarded as heat sources, and a person name
which has strong connection with the query (i.e., frequently co-occur with query keywords and
co-occur with other names related to query keywords) will receive most of the heat, thus being
ranked high. Experiments on the ClueWeb09 web collection show that our algorithm is effective
for retrieving experts and outperforms baseline algorithms significantly. This work would be
regarded as one step toward addressing the more general entity search problem without
sophisticated NLP techniques.

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7. Clustering Uncertain Data Based on Probability Distribution Similarity
Abstract :
Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts
significant challenges on both modeling similarity between uncertain objects and developing
efficient computational methods. The previous methods extend traditional partitioning clustering
methods like k-means and density-based clustering methods like DBSCAN to uncertain data,
thus rely on geometric distances between objects. Such methods cannot handle uncertain objects
that are geometrically indistinguishable, such as products with the same mean but very different
variances in customer ratings. Surprisingly, probability distributions, which are essential
characteristics of uncertain objects, have not been considered in measuring similarity between
uncertain objects. In this paper, we systematically model uncertain objects in both continuous
and discrete domains, where an uncertain object is modeled as a continuous and discrete random
variable, respectively. We use the well-known Kullback-Leibler divergence to measure similarity
between uncertain objects in both the continuous and discrete cases, and integrate it into
partitioning and density-based clustering methods to cluster uncertain objects. Nevertheless, a
naı¨ve implementation is very costly. Particularly, computing exact KL divergence in the
continuous case is very costly or even infeasible. To tackle the problem, we estimate KL
divergence in the continuous case by kernel density estimation and employ the fast Gauss
transform technique to further speed up the computation. Our extensive experiment results verify
the effectiveness, efficiency, and scalability of our approaches.

8. PMSE: A Personalized Mobile Search Engine
Abstract :
We propose a personalized mobile search engine (PMSE) that captures the users' preferences
in the form of concepts by mining their clickthrough data. Due to the importance of location
information in mobile search, PMSE classifies these concepts into content concepts and location
concepts. In addition, users' locations (positioned by GPS) are used to supplement the location
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concepts in PMSE. The user preferences are organized in an ontology-based, multifacet user
profile, which are used to adapt a personalized ranking function for rank adaptation of future
search results. To characterize the diversity of the concepts associated with a query and their
relevances to the user's need, four entropies are introduced to balance the weights between the
content and location facets. Based on the client-server model, we also present a detailed
architecture and design for implementation of PMSE. In our design, the client collects and stores
locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction,
training, and reranking are performed at the PMSE server. Moreover, we address the privacy
issue by restricting the information in the user profile exposed to the PMSE server with two
privacy parameters. We prototype PMSE on the Google Android platform. Experimental results
show that PMSE significantly improves the precision comparing to the baseline.

9. Discovering Temporal Change Patterns in the Presence of Taxonomies
Abstract :
Frequent itemset mining is a widely exploratory technique that focuses on discovering
recurrent correlations among data. The steadfast evolution of markets and business environments
prompts the need of data mining algorithms to discover significant correlation changes in order
to reactively suit product and service provision to customer needs. Change mining, in the context
of frequent itemsets, focuses on detecting and reporting significant changes in the set of mined
itemsets from one time period to another. The discovery of frequent generalized itemsets, i.e.,
itemsets that 1) frequently occur in the source data, and 2) provide a high-level abstraction of the
mined knowledge, issues new challenges in the analysis of itemsets that become rare, and thus
are no longer extracted, from a certain point. This paper proposes a novel kind of dynamic
pattern, namely the HIstory GENeralized Pattern (HIGEN), that represents the evolution of an
itemset in consecutive time periods, by reporting the information about its frequent
generalizations characterized by minimal redundancy (i.e., minimum level of abstraction) in case
it becomes infrequent in a certain time period. To address HIGEN mining, it proposes HIGEN
MINER, an algorithm that focuses on avoiding itemset mining followed by postprocessing by
exploiting a support-driven itemset generalization approach. To focus the attention on the
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minimally redundant frequent generalizations and thus reduce the amount of the generated
patterns, the discovery of a smart subset of HIGENs, namely the NONREDUNDANT HIGENs,
is addressed as well. Experiments performed on both real and synthetic datasets show the
efficiency and the effectiveness of the proposed approach as well as its usefulness in a real
application context.

10.Spatial Approximate String Search
Abstract :

This work deals with the approximate string search in large spatial databases. Specifically,
we investigate range queries augmented with a string similarity search predicate in both
euclidean space and road networks. We dub this query the spatial approximate string (SAS)
query. In euclidean space, we propose an approximate solution, the MHR-tree, which embeds
min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise
representation of the union of q-grams from strings under the subtree of u. We analyze the
pruning functionality of such signatures based on the set resemblance between the query string
and the q-grams from the subtrees of index nodes. We also discuss how to estimate the
selectivity of a SAS query in euclidean space, for which we present a novel adaptive algorithm to
find balanced partitions using both the spatial and string information stored in the tree. For
queries on road networks, we propose a novel exact method, RSASSOL, which significantly
outperforms the baseline algorithm in practice. The RSASSOL combines the q-gram-based
inverted lists and the reference nodes based pruning. Extensive experiments on large real data
sets demonstrate the efficiency and effectiveness of our approaches.

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11.Robust Module-Based Data Management
Abstract :

The current trend for building an ontology-based data management system (DMS) is to
capitalize on efforts made to design a preexisting well-established DMS (a reference system).
The method amounts to extracting from the reference DMS a piece of schema relevant to the
new application needs—a module—, possibly personalizing it with extra constraints w.r.t. the
application under construction, and then managing a data set using the resulting schema. In this
paper, we extend the existing definitions of modules and we introduce novel properties of
robustness that provide means for checking easily that a robust module-based DMS evolves
safely w.r.t. both the schema and the data of the reference DMS. We carry out our investigations
in the setting of description logics which underlie modern ontology languages, like RDFS, OWL,
and OWL2 from W3C. Notably, we focus on the DL-liteA dialect of the DL-lite family, which
encompasses the foundations of the QL profile of OWL2 (i.e., DL-liteR): the W3C
recommendation for efficiently managing large data sets.

12.Protecting Sensitive Labels in Social Network Data Anonymization
Abstract :

Privacy is one of the major concerns when publishing or sharing social network data for
social science research and business analysis. Recently, researchers have developed privacy
models similar to k-anonymity to prevent node reidentification through structure information.
However, even when these privacy models are enforced, an attacker may still be able to infer
one’s private information if a group of nodes largely share the same sensitive labels (i.e.,
attributes). In other words, the label-node relationship is not well protected by pure structure
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anonymization methods. Furthermore, existing approaches, which rely on edge editing or node
clustering, may significantly alter key graph properties. In this paper, we define a k-degree-ldiversity anonymity model that considers the protection of structural information as well as
sensitive labels of individuals. We further propose a novel anonymization methodology based on
adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph
with the consideration of introducing the least distortion to graph properties. Most importantly,
we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and
their impacts on an important graph property. We conduct extensive experiments to evaluate the
effectiveness of the proposed technique.

13.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 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,
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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.

14.A Fast Clustering-Based Feature Subset Selection Algorithm for HighDimensional Data
Abstract :

Feature selection involves identifying a subset of the most useful features that produces
compatible results as the original entire set of features. A feature selection algorithm may be
evaluated from both the efficiency and effectiveness points of view. While the efficiency
concerns the time required to find a subset of features, the effectiveness is related to the
quality of the subset of features. Based on these criteria, a fast clustering-based feature
selection algorithm (FAST) is proposed and experimentally evaluated in this paper. The FAST
algorithm works in two steps. In the first step, features are divided into clusters by using
graph-theoretic clustering methods. In the second step, the most representative feature that is
strongly related to target classes is selected from each cluster to form a subset of features.
Features in different clusters are relatively independent, the clustering-based strategy of FAST
has a high probability of producing a subset of useful and independent features. To ensure the
efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.
The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical
study. Extensive experiments are carried out to compare FAST and several representative
feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with
respect to four types of well-known classifiers, namely, the probabilitybased Naive Bayes, the
tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature
selection. The results, on 35 publicly available real-world high-dimensional image,
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microarray, and text data, demonstrate that the FAST not only produces smaller subsets of
features but also improves the performances of the four types of classifiers.

15.Ranking on Data Manifold with Sink Points
Abstract :

Ranking is an important problem in various applications, such as Information Retrieval (IR),
natural language processing, computational biology, and social sciences. Many ranking
approaches have been proposed to rank objects according to their degrees of relevance or
importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in
ranking. Top ranked results are expected to convey as little redundant information as possible,
and cover as many aspects as possible. However, existing ranking approaches either take no
account of diversity, or handle it separately with some heuristics. In this paper, we introduce a
novel approach, Manifold Ranking with Sink Points (MRSPs), to address diversity as well as
relevance and importance in ranking. Specifically, our approach uses a manifold ranking process
over the data manifold, which can naturally find the most relevant and important data objects.
Meanwhile, by turning ranked objects into sink points on data manifold, we can effectively
prevent redundant objects from receiving a high rank. MRSP not only shows a nice convergence
property, but also has an interesting and satisfying optimization explanation. We applied MRSP
on two application tasks, update summarization and query recommendation, where diversity is of
great concern in ranking. Experimental results on both tasks present a strong empirical
performance of MRSP as compared to existing ranking approaches.

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16.Tweet Analysis for Real-Time Event Detection and Earthquake Reporting
System Development
Abstract :

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.

17.Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering
Algorithm

Abstract :

In comparison with hard clustering methods, in which a pattern belongs to a single cluster,
fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of
membership. This is important in domains such as sentence clustering, since a sentence is likely
to be related to more than one theme or topic present within a document or set of documents.
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However, because most sentence similarity measures do not represent sentences in a common
metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of
Gaussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy
clustering algorithm that operates on relational input data; i.e., data in the form of a square
matrix of pairwise similarities between data objects. The algorithm uses a graph representation of
the data, and operates in an Expectation-Maximization framework in which the graph centrality
of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to
sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping
clusters of semantically related sentences, and that it is therefore of potential use in a variety of
text mining tasks. We also include results of applying the algorithm to benchmark data sets in
several other domains.

18.Distributed Processing of Probabilistic Top-k Queries in Wireless Sensor
Networks
Abstract :

In this paper, we introduce the notion of sufficient set and necessary set for distributed
processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two
concepts have very nice properties that can facilitate localized data pruning in clusters.
Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary setbased (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds
of communications. Moreover, in responding to dynamic changes of data distribution in the
network, we develop an adaptive algorithm that dynamically switches among the three proposed
algorithms to minimize the transmission cost. We show the applicability of sufficient set and
necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured
network topologies. Experimental results show that the proposed algorithms reduce data
transmissions significantly and incur only small constant rounds of data communications. The
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