IEEE Mobile computing Title and Abstract 2016

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2016 TOPICS
Enhanced Deployment Algorithms for Heterogeneous Directional Mobile Sensors in a
Bounded Monitoring Area
Abstract - Good deployment of sensors empowers the network with effective monitoring ability.
Different from omnidirectional sensors, the coverage region of a directional sensor is determined
by not only the sensing radius (distance), but also its sensing orientation and spread angle.
Heterogeneous sensing distances and spread angles are likely to exist among directional sensors,
to which we refer as heterogeneous directional sensors. In this paper, we target on a bounded
monitoring area and deal with heterogeneous directional sensors equipped with locomotion and
rotation facilities to enable the sensors self-deployment. Two Enhanced Deployment Algorithms,
EDA-I and EDA-II, are proposed to achieve high sensing coverage ratio in the monitored field.
EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary
torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movements
and boundary torques guided rotations. EDA-I computations can be centralized or distributed
that differ in required energy and execution time, whereas EDA-II only allows centralized
calculations. Our EDA-II outperforms EDA-I in centralized operations, while EDA-I can be
adapted into a distributed deployment algorithm without requiring global information and still
achieves comparably good coverage performance to its centralized version. To the best of our
knowledge, this is perhaps the first work to employ movements followed by rotations for sensors
self-deployment. Performance results demonstrate that our enhanced deployment mechanisms
are capable of providing desirable surveillance level, while consuming moderate moving and
rotating energy under reasonable execution time.
IEEE Transactions on Mobile Computing (May 2016)
Game User-Oriented Multimedia Transmission over Cognitive Radio Networks
Abstract - Cognitive radio (CR) is an emerging technique to improve the efficiency of spectrum
resource utilization. In CR networks, the selfish behavior of secondary users (SU) can
considerably affect the performance of primary users (PU). Accordingly, game theory, which
takes into consideration of the game players’ selfish behavior, has been applied into the design of

CR networks. Most of the existing studies focus on the network design only from the network
perspective to improve system performance such as utility and throughput. However, the users’
experience to the service, which cannot simply be reflected by quality of service (QoS), has been
largely ignored. The user-perceived multimedia quality and service can be different from the
actual received multimedia quality, and thus is very important to take into consider of the
network design. To better serve the network users, quality of experience (QoE) is adopted to
measure the network service from the users’ perspective and help improve the users’ satisfaction
to the CR network service. As CR networks requires lots of data storage and computation for
spectrum sensing, spectrum sharing and algorithm design, cloud computation comes as a handy
solution because it can provide massive storage and fast computation. In this paper, we propose
to design a user-oriented CR cloud network for multimedia applications, where the user’s
satisfaction is reflected in the CR cloud network design. In the proposed framework, the primary
and secondary user game is formulated as Stackelberg game. Specifically, a refunding term is
defined in the users’ utility function to effectively consider and to reflect the network users’ QoE
requirement. Our contributions include two folds: (1) A game based CR cloud network design
for multimedia transmission is proposed, and the network user’s QoE requirement is satisfied in
the design; (2) The existence and uniqueness of the Stackelberg Nash equili- rium is proved, and
the design is optimal. Our simulation results demonstrate the effectiveness of the game user-
oriented CR cloud network design.
IEEE Transactions on Circuits and Systems for Video Technology (May 2016 )
Mobile Demand Profiling for Cellular Cognitive Networking
Abstract - In the next few years, mobile networks will undergo significant evolutions in order to
accommodate the ever-growing load generated by increasingly pervasive smartphones and
connected objects. Among those evolutions, cognitive networking upholds a more dynamic
management of network resources that adapts to the significant spatiotemporal fluctuations of the
mobile demand. Cognitive networking techniques root in the capability of mining large amounts
of mobile traffic data collected in the network, so as to understand the current resource utilization
in an automated manner. In this paper, we take a first step towards cellular cognitive networks by
proposing a framework that analyzes mobile operator data, builds profiles of the typical demand,
and identifies unusual situations in network-wide usages. We evaluate our framework on two

real-world mobile traffic datasets, and show how it extracts from these a limited number of
meaningful mobile demand profiles. In addition, the proposed framework singles out a large
number of outlying behaviors in both case studies, which are mapped to social events or
technical issues in the network.
IEEE Transactions on Mobile Computing (May 2016 )
Real-time Tele-monitoring of Patients with Chronic Heart-Failure Using a Smartphone:
Lessons Learned
Abstract - We present a smartphone-based system for remote real-time tele-monitoring of
physical activity in patients with chronic heart-failure (CHF). We recently completed a pilot
study with 15 subjects to evaluate the feasibility of the proposed monitoring in the real world and
examine its requirements, privacy implications, usability, and other challenges encountered by
the participants and healthcare providers. Our tele-monitoring system was designed to assess
patient activity via minute-byminute energy expenditure (EE) estimated from accelerometry. In
addition, we tracked relative user location via global positioning system (GPS) to track outdoors
activity and measure walking distance. The system also administered daily-surveys to inquire
about vital signs and general cardiovascular symptoms. The collected data were securely
transmitted to a central server where they were analyzed in real time and were accessible to the
study medical staff to monitor patient health status and provide medical intervention if needed.
Although the system was designed for tele-monitoring individuals with CHF, the challenges,
privacy considerations, and lessons learned from this pilot study apply to other chronic health
conditions, such as diabetes and hypertension, that would benefit from continuous monitoring
through mobile-health (mHealth) technologies.
IEEE Transactions on Affective Computing (April 2016 )
Robotic Message Ferrying for Wireless Networks using Coarse-Grained Backpressure
Control
Abstract - We formulate the problem of robots ferrying messages between statically-placed
source and sink pairs that they can communicate with wirelessly. We first analyze the capacity
region for this problem under ideal conditions. We indicate how robots could be scheduled
optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival
rate. We then consider the setting where the arrival rate is unknown and present a coarse-grained

backpressure message ferrying algorithm (CBMF) for it. In CBMF, the robots are matched to
sources and sinks once every epoch to maximize a queue-differential-based weight. The
matching controls both motion and transmission for each robot. We show through analysis and
simulations the conditions under which CBMF can stabilize the network, and its corresponding
delay performance. From a practical point of view, we propose a heuristic approach to adapt the
epoch duration according to network conditions that can improve the end-to-end delay while
guaranteeing the network stability at the same time. We also study the structural properties with
its explicit delay performance of the CBMF algorithm in a homogeneous network.
IEEE Transactions on Mobile Computing (April 2016 )
Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications:
Performance and Tradeoffs
Abstract - In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base
station used to provide the fly wireless communications to a given geographical area is analyzed.
In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an
underlaid device-to-device (D2D) communication network is considered. For this model, a
tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are
considered: a static UAV and a mobile UAV. In the first scenario, the average coverage
probability and the system sum-rate for the users in the area are derived as a function of the UAV
altitude and the number of D2D users. In the second scenario, using the disk covering problem,
the minimum number of stop points that the UAV needs to visit in order to completely cover the
area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D
users, the overall outage probability of the D2D users is derived. Simulation and analytical
results show that, depending on the density of D2D users, the optimal values for the UAV
altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover,
our results also show that, by enabling the UAV to intelligently move over the target area, the
total required transmit power of UAV while covering the entire area, can be minimized. Finally,
in order to provide full coverage for the area of interest, the tradeoff between the coverage and
delay, in terms of the number of stop points, is discussed.
IEEE Transactions on Wireless Communications (June 2016)

ClickSmart: A Context-Aware Viewpoint Recommendation System for Mobile
Photography
Abstract - In this paper we propose ClickSmart, a viewpoint recommendation system which can
assist a user in capturing high quality photographs at well known tourist locations. ClickSmart
can provide real-time viewpoint recommendation based on the preview on user’s camera, current
time and user’s geo-location. It makes use of publicly available geo-tagged images along with
associated meta data for learning a recommendation model. We define view-cells, macro blocks
in geo-space, and propose the concepts of popularity, quality and uniqueness of view-cells from
the viewpoint perspective. Viewpoint recommendation is generated at the granularity of a view-
cell and is based on its popularity, quality and uniqueness, which are estimated using social
media cues associated with images. We further observe that contextual information such as time
and weather conditions play an important role in photography, and therefore augment the
recommendation system with associated context. ClickSmart also takes into account presence of
people in the view for making the recommendation. It can provide two kinds of
recommendations, quality-based and uniqueness-based. Although both were found effective in
experimental evaluation, our user study showed that uniqueness based recommendation was
preferred more by skilled photographers as compared to amateurs.
IEEE Transactions on Circuits and Systems for Video Technology (April 2016 )
Energy Aware Offloading for Competing Users on a Shared Communication Channel
This paper considers a set of mobile users that employ cloud-based computation offloading. In
order to execute jobs in the cloud however, the user uploads must occur over a base station
channel that is shared by all of the uploading users. Since the job completion times are subject to
hard deadline constraints, this restricts the feasible set of jobs that can be processed. The system
is modelled as a competitive game in which each user is interested in minimizing its own energy
consumption. The game is subject to the real-time constraints imposed by the job execution
deadlines, user specific channel bit rates, and the competition over the shared communication
channel. The paper shows that for a wide range of parameters, a game where each user
independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-
Seidel-like method for determining this equilibrium is introduced. Results are presented that
illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method.

Data is also presented that show the number of iterations required, and the quality of the
solutions. We find that the solutions perform well compared to a lower bound on total energy
performance.
IEEE Transactions on Mobile Computing (March 2016)
Adaptive Prefetching in Wireless Computing
Abstract - In this paper, we consider a basic issue in wireless computing, where mobile devices
(with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points
over wireless channels of fluctuating quality. When the channel quality is low, slow data
downloads can contribute to application latency, and degradation of the user experience. To
mitigate this issue, mobile devices can prefetch data during good quality channel periods
preemptively, in anticipation of using them during low quality channel epochs. In other words,
under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively
to reduce application latency when channel conditions have degraded. Considering channel
fluctuations, memory constraints, and application latency, the issue at each point in time is
whether to prefetch or not. A dynamic programming approach is taken here to determine the
optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-
Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm,
which uses a randomized approximation of FON, thus lifting the need for online optimization
and substantially reducing the computational complexity. Simulations are used to demonstrate
that our low-complexity schemes perform well when compared to the optimal. In addition, our
schemes outperform benchmark design techniques in realistic channel conditions.
IEEE Transactions on Wireless Communications (May 2016)
Energy Efficient Resource Allocation and User Scheduling for Collaborative Mobile
Clouds with Hybrid Receivers
Abstract - In this paper, we study the resource allocation and user scheduling algorithm for
minimizing the energy cost of data transmission in the context of OFDMA collaborative mobile
cloud (CMC) with simultaneous wireless information and power transfer (SWIPT) receivers. The
CMC, which consists of several collaborating mobile terminals (MTs) offers one potential
solution for downlink content distribution and for the energy consumption reduction. Previous
work on the design of CMC system mainly focused on the cloud formulation or EE investigation,

while how to allocate the radio resource and schedule user transmission lacks attention. With the
objective to minimize the system energy consumption, an optimization problem which jointly
considers subchannel assignment, power allocation and user scheduling has been presented. We
propose different algorithms to address the formulated problem based on the convex
optimization technique. Simulation results demonstrate that the proposed user scheduling and
resource allocation algorithms can achieve significant EE performance.
IEEE Transactions on Vehicular Technology (February 2016)
Compress-then-Analyze vs Analyze-then-Compress: what is best in Visual Sensor
Networks
Visual Sensor Networks (VSNs) have attracted the interest of researchers worldwide in the last
few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT).
When used to perform visual analysis tasks, VSNs may be operated according to two different
paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed
and transmitted for further analysis. Conversely, in the analyze-then-compress paradigm, image
features are extracted by visual sensor nodes, encoded and then delivered to a remote destination
where analysis is performed. The question this paper aims to answer is What is the best visual
analysis paradigm in VSNs? To do this, first we empirically characterize the rate-energy-
accuracy performance of the two aforementioned paradigms. Then, we leverage such models to
formulate a resource allocation problem for VSNs. The problem optimally allocates the specific
paradigm used by each camera node in the network and the related transmission source rate, with
the objective of optimizing the accuracy of the visual analysis task and the VSN coverage.
Experimental results over several VSNs instances demonstrate that there is no “winning”
paradigm, but the best performance are obtained by allowing the coexistence of the two and by
properly optimizing their utilization.
IEEE Transactions on Mobile Computing (January 2016)
Pattern Prediction and Passive Bandwidth Management for Hand-Over Optimization in
QoS Cellular Networks with Vehicular Mobility
Abstract - In wireless networking the main desire of end-users is to take advantage of
satisfactory services, in terms of QoS, especially when they pay for a required need. Many

efforts have been made to investigate how the continuity of services can be guaranteed in QoS
networks, where users can move from one cell to another one. The introduction of a prediction
scheme with passive reservations is the only way to face this issue; however, the deployment of
in-advance bandwidth leads the system to waste resources. This work consists of two main
integrated contributions: a new pattern prediction scheme based on a distributed set of Markov
chains, in order to handle passive reservations, and a statistical bandwidth management
algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed
Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the
considered technology and the vehicular environment. Several simulation campaigns were
conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with
other prediction schemes, in terms of system utilization, accuracy, call dropping and call
blocking probabilities.
Frame Interpolation for Cloud-Based Mobile Video Streaming
Abstract - Cloud-based High Definition (HD) video streaming is becoming popular day by
day. On one hand, it is important for both end users and large storage servers to store their huge
amount of data at different locations and servers. On the other hand, it is becoming a big
challenge for network service providers to provide reliable connectivity to the network users.
There have been many studies over cloud-based video streaming for Quality of Experience
(QoE) for services like YouTube. Packet losses and bit errors are very common in transmission
networks, which affect the user feedback over cloud-based media services. To cover up packet
losses and bit errors, Error Concealment (EC) techniques are usually applied at the
decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and
quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded
videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach
is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search
process for the lost MVs, a bigger block size and searching in parallel are both considered. The
simulation results clearly show that our proposed method outperforms the traditional Block
Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a
packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The

computational time of the proposed approach outperforms the BMA by approximately 1788
seconds.
IEEE Transactions on Multimedia (May 2016)
On Improving Wireless Channel Utilization: A Collision Tolerance-based Approach
Packet corruption caused by collision is a critical problem that hurts the performance of wireless
networks. Conventional medium access control (MAC) protocols resort to collision avoidance to
maintain acceptable efficiency of channel utilization. According to our investigation and
observation, however, collision avoidance comes at the cost of miscellaneous overhead, which
oppositely hurts channel utilization, not to mention the poor resiliency and performance of those
protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate
collisions at the physical layer implementations of wireless networks, we in this paper propose
Coco, a protocol that advocates simultaneous accesses from multiple senders to a shared channel,
i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but
effective design, Coco addresses the key challenges in achieving collision tolerance, such as
precise sender alignment and the control of transmission concurrency. We implement Coco in
802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB
nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at
least 20% in general cases, compared with state-of-the-arts protocols.
Matched Channel Allocation for Advanced Metering Infrastructure based on Cognitive
Mobile Virtual Network Operator
Abstract - This paper analyses a matched channel allocation algorithm for Advanced
Metering Infrastructure (AMI) for Smart Grid (SG) with connectivity provided by a Cognitive
Mobile Virtual Network Operator (C-MVNO). The proposed algorithm studies the behavior of
secondary-spectrum channels assigned to the AMI using the cellular network. In this work, we
consider the Smart Meter (SM) as a device-cell without mobility with capabilities of connecting
and disconnecting the output electric power. According to the efficiency and reliability
requirements of Smart Grid, we consider the spectrum reuse in the MVNO to avoid the need of
deployment the communications infrastructure of the AMI from cero. Here, we optimize the
dynamic channel assignment by taking advantage of white holes present in the spectrum using a

cognitive radio. This work, articulates the coexistence of different types of uplink channels, such
as random, reserved and fixed, and a strategic model with C-MVNO, MVNO, based on
traditional cellular networks.
IEEE Latin America Transactions (June 2016)
GreenCoMP: Energy-Aware Cooperation for Green Cellular Networks
Abstract - Switching off base stations (BSs) is an effective and efficient energy-saving
solution for green cellular networks. The previous works focus mainly on when to switch off BSs
without sacrificing the traffic demands of current active users, and then enlarge the coverage of
the stay-on cells to cover as much more users as possible. Based on this objective, both constant
power and transmission power of each BS become the major energy consumption sources.
However, the transmission powers of enlarged cells, which have not been taken into account in
previous research, are not negligible as compared to other energy consumption sources. To
tackle this problem, we observe that the transmission power of one specific BS could be reduced
via cooperation among two or more BSs, which is typically used to improve the throughput or
enhance the spectrum efficiency in wireless systems. The challenges come mainly from how to
jointly consider which BSs to switch off and how to cooperate among active-mode BSs. In this
paper, we design energy-aware cooperation strategies that ensure that our system is energy-
saving while satisfying user demands. To cope with sleep-mode BSs and perform cooperation
among active BSs, we formulate this problem as a binary integer programming problem, and
prove it is NP-hard. Based on our formulation, we derive a performance lower bound for this
problem via Lagrangian Relaxation with search enumeration. Furthermore, we propose two
heuristic algorithms accounting for the properties of energy savings and the constraints of
bandwidth resources. The simulation results show that our algorithms outperform pure power
control mechanisms that do not consider the transmission power and pure cooperation without
power control in terms of the total consumed energy. We also observe that larger cooperative
size does not imply a better strategy under different scenarios. Compared to the total consumed
energy given that all BSs are turned on, our algorithms can save up to 60%- of energy. This

demonstrates that our methods are indeed efficient energy-saving cooperation strategies for
green cellular networks.
IEEE Transactions on Mobile Computing (March 2016
Fair and Efficient Coexistence of Heterogeneous Channel Widths in Next-Generation
Wireless LANs
Abstract - To meet the diverse traffic demands of different applications, emerging WLAN
standards have been incorporating a variety of channel widths ranging from 5MHz to 160MHz.
The coexistence of variable-width channels imposes a new challenge to the 802.11 protocols,
since the 802.11 MAC is agnostic of, and thus incapable of, adapting to the PHY-layer spectrum
heterogeneity. To address this challenge, we uncover the cause and effect of variable-width
channel coexistence, and develop a MAC-layer scheme, called Fine-grained Spectrum Sharing
(FSS), that solves the general problem of fair and efficient spectrum sharing among users with
heterogeneous channel-widths. Instead of deeming its spectrum band as an atomic block, an FSS
user divides the spectrum into chunks, and adapts its chunk usage on a per-packet basis. FSS’s
spectrum adaptation is driven by a decentralized optimization framework. It preserves the 802.11
CSMA/CA primitives while allowing users to contend for each spectrum chunk, and can
opportunistically split a wide-band channel or bond multiple (discontiguous) chunks to ensure
fair and efficient access to available spectrum. In making such adaptation decisions, it balances
the benefit from discontiguous chunks and the cost of guardband – a unique tradeoff in WLANs
with heterogeneous channel-widths. Our in-depth evaluation demonstrates that FSS can improve
throughput by multiple folds, while maintaining fairness of spectrum sharing for heterogeneous
WLANs.
Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks
Abstract - Self-organizing networks (SON) aim at simplifying network management (NM)
and optimizing network capital and operational expenditure through automation. Most SON
functions (SFs) are rule-based control structures, which evaluate metrics and decide actions
based on a set of rules. These rigid structures are, however, very complex to design since rules
must be derived for each SF in each possible scenario. In practice, rules only support generic

behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON
coordination becomes very complicated with such varied control structures. In this paper, we
propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that
enables the SFs to independently learn the required optimal configurations. We propose a
generalized Q-learning framework for the CCN functions and show how the framework fits to a
general SF control loop. We then apply this framework to two functions on mobility robustness
optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO
function learns to optimize handover performance while the MLB function learns to distribute
instantaneous load among cells.
IEEE Transactions on Network and Service Management (March 2016)
On the Serviceability of Mobile Vehicular Cloudlets in a Large-Scale Urban Environment
Abstract - Recently, cloud computing technology has been utilized to make vehicles on roads
smarter and to offer better driving experience. Consequently, the concept of mobile vehicular
cloudlet (MVC) was born, where nearby smart vehicles were connected to provide cloud
computing services locally. Existing researches focus on MVC system models and architectures,
and no work to date addresses the critical question of what is the potential, i.e., level of local
cloud computing service, achievable by MVCs in real-world large-scale urban environments.
This issue is fundamental to the practical implementation of MVC technology. Answering this
question is also challenging because MVCs operate in highly complicated and dynamic
environments. In this paper, we directly address this challenging issue and we introduce the
concept of serviceability to measure the ability of an MVC to provide cloud computing service.
In particular, we evaluate this measure in practical environments through a real-world vehicular
mobility trace of Beijing. Using the time-varying graph model for mobile cloud computing under
different scenarios, we find that the serviceability has a relationship with the delay tolerance of
the undertaken computational task, which can be described by two characteristic parameters. The
evolution of serviceability through a day and the influence of network congestion are also
analyzed. We also portray the spatial distribution of the serviceability and analyze the influence
of connectivity and mobility in both MVC and vehicle levels. Our observations are valuable to
assist designing vehicular cloud computing systems and applications, as well as to help make
offloading decisions.

IEEE Transactions on Intelligent Transportation Systems (June 2016)
Dynamic Channel Bonding: Enabling Flexible Spectrum Aggregation
Abstract - To support applications that demand high-speed wireless communication, the fifth
generation (5G) Wi-Fi increases the channel bonding from 40 MHz in 802.11n to 80, and even
160 MHz under certain conditions in 802.11ac. However, inefficiency and unfairness issues arise
when devices that use different channel widths coexist in a contention domain. In this paper, we
propose a dynamic channel bonding (DyB) protocol in which a node is allowed to start a
transmission as long as there are some idle narrow channels and it gradually increases the
channel width during transmission whenever new narrow channels become available. To enable
communication over uncertain channels, a convolution method is introduced to achieve fast
spectrum agreement between the transmitter and the receiver. In addition, DyB considers the
severe contention in a wide band of spectrum. A compound preamble is designed to make
collisions detectable in the frequency domain and a parallel bitwise arbitration is designed to
quickly resolve the collisions in the time domain. We implement and evaluate the DyB through
both the GNU Radio/USRP platform and ns-2 simulations. Experimental results and simulations
show that DyB well addresses the inefficiency and unfairness issues caused by heterogeneous
radio coexistence.
IEEE Transactions on Mobile Computing (February 2016)
Efficient Tracking Area Management Framework for 5G Networks
Abstract - One important objective of 5G mobile networks is to accommodate a diverse and
ever-increasing number of user equipment (UEs). Coping with the massive signaling overhead
expected from UEs is an important hurdle to tackle so as to achieve this objective. In this paper,
we devise an efficient tracking area list management (ETAM) framework that aims to find
optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them
to UEs, with the objective of minimizing two conflicting metrics, namely paging overhead and
tracking area update (TAU) overhead. ETAM incorporates two parts (online and offline) to
achieve its design goal. In the online part, two strategies are proposed to assign in real time,
TALs to different UEs, while in the offline part, three solutions are proposed to optimally
organize TAs into TALs. The performance of ETAM is evaluated via analysis and simulations,

and the obtained results demonstrate its feasibility and ability in achieving its design goals,
improving the network performance by minimizing the cost associated with paging and TAU.
IEEE Transactions on Wireless Communications (June 2016
Effects of Content Popularity on the Performance of Content-Centric Opportunistic
Networking: An Analytical Approach and Applications
Abstract - Mobile users are envisioned to exploit direct communication opportunities between
their portable devices, in order to enrich the set of services they can access through cellular or
WiFi networks. Sharing contents of common interest or providing access to resources or services
between peers can enhance a mobile node's capabilities, offload the cellular network, and
disseminate information to nodes without Internet access. Interest patterns, i.e., how many nodes
are interested in each content or service (popularity), as well as how many users can provide a
content or service (availability) impact the performance and feasibility of envisioned
applications. In this paper, we establish an analytical framework to study the effects of these
factors on the delay and success probability of a content/service access request through
opportunistic communication. We also apply our framework to the mobile data offloading
problem and provide insights for the optimization of its performance. We validate our model and
results through realistic simulations, using datasets of real opportunistic networks.
IEEE/ACM Transactions on Networking (February 2016)
NoPSM: A Concurrent MAC Protocol over Low-data-rate Low-power Wireless Channel
without PRR-SINR Model
Abstract - Concurrent MAC protocols can improve channel usage of wireless sensor networks
(WSNs), and provide a high-performance infrastructure for data intensive applications. Most of
existing concurrent MAC protocols are based on proactively constructed physical interference
models, i.e. PRR-SINR models (PSM). However, it incurs relatively high bandwidth and energy
overheads to construct PSM for WSNs. In this paper, we propose NoPSM, which does not take
PSM as base to determine transmission concurrency. Instead, the base of NoPSM is reactively
constructed interference relationships by passively analyzing overlapping relationships among
time logs of block data transmissions and corresponding reception status of each packet in

blocks. In this way, NoPSM has two salient features. Firstly, NoPSM is able to construct
interference relationships among nodes quickly and accurately along with block data
transmissions without needs of network downtime. Secondly, based on the constructed
interference relationships, NoPSM can make decisions of transmission concurrency with a
comprehensive criterion, which not only estimates quality of any active links after initiating a
new link, but also estimates throughput improvement gained from concurrent transmissions.
NoPSM has been implemented in Tinyos-2.1 and extensively evaluated in TOSSIM.
Experimental results show that NoPSM improves system throughput by up to 60% compared
with a traditional CSMA protocol, which cannot exploit potential transmission concurrency.
Moreover, NoPSM can gain up to 55% throughput improvement as compared to an existing
reactive concurrent MAC.
Secret Common Randomness From Routing Metadata in Ad Hoc Networks
Abstract - Establishing secret common randomness between two or multiple devices in a
network resides at the root of communication security. In its most frequent form of key
establishment, the problem is traditionally decomposed into a randomness generation stage
(randomness purity is subject to employing often costly true random number generators) and an
information-exchange agreement stage, which relies either on public-key infrastructure or on
symmetric encryption (key wrapping). In this paper, we propose a secret-common-randomness
establishment algorithm for ad hoc networks, which works by harvesting randomness directly
from the network routing metadata, thus achieving both pure randomness generation and
(implicitly) secret-key agreement. Our algorithm relies on the route discovery phase of an ad hoc
network employing the dynamic source routing protocol, is lightweight, and requires relatively
little communication overhead. The algorithm is evaluated for various network parameters in an
OPNET ad hoc network simulator. Our results show that, in just 10 min, thousands of secret
random bits can be generated network-wide, between different pairs in a network of 50 users.
IEEE Transactions on Information Forensics and Security (Aug. 2016)
An Optical Flow-Based Full Reference Video Quality Assessment Algorithm

Abstract - We present a simple yet effective optical flow-based full-reference video quality
assessment (FR-VQA) algorithm for assessing the perceptual quality of natural videos. Our
algorithm is based on the premise that local optical flow statistics are affected by distortions and
the deviation from pristine flow statistics is proportional to the amount of distortion. We
characterize the local flow statistics using the mean, the standard deviation, the coefficient of
variation (CV), and the minimum eigenvalue (λmin) of the local flow patches. Temporal distortion
is estimated as the change in the CV of the distorted flow with respect to the reference flow, and
the correlation between λmin of the reference and of the distorted patches. We rely on the robust
multi-scale structural similarity index for spatial quality estimation. The computed temporal and
spatial distortions, thus, are then pooled using a perceptually motivated heuristic to generate a
spatio-temporal quality score. The proposed method is shown to be competitive with the state-of-
the-art when evaluated on the LIVE SD database, the EPFL Polimi SD database, and the LIVE
Mobile HD database. The distortions considered in these databases include those due to
compression, packet-loss, wireless channel errors, and rate-adaptation. Our algorithm is flexible
enough to allow for any robust FR spatial distortion metric for spatial distortion estimation. In
addition, the proposed method is not only parameter-free but also independent of the choice of
the optical flow algorithm. Finally, we show that the replacement of the optical flow vectors in
our proposed method with the much coarser block motion vectors also results in an acceptable
FR-VQA algorithm. Our algorithm is called the flow similarity index.
IEEE Transactions on Image Processing (June 2016
Intra-MARIO: A Fast Mobility Management Protocol for 6LoWPAN
Abstract - One of the major challenges in 6LoWPAN is to provide continuous services while
mobile nodes’ movements with minimizing network inaccessible time caused due to handoffs.
Even though MIPv6, HMIPv6, and PMIPv6 are commonly accepted standards to address this in
IP networks, they cannot inherently avoid the degradation in communication quality during
handoff, since they are not designed with consideration of constrained node networks like
6LoWPAN. In this paper, we propose a new fast mobility management protocol for 6LoWPAN,
named intra-MARIO. To minimize handoff delay and enhance service availability, intra-MARIO
introduces three important components, which are a fast rejoin scheme for handoff management

with an adaptive polling-based movement detection and multi-hop pointer forwarding schemes
for location management. To justify the effectiveness, we have conducted the extensive
simulations by comparing intra-MARIO with prior schemes like a basic mobility management
scheme and a PMIPv6-based protocol. We then implement intra-MARIO on top of our
6LoWPAN platform (SNAIL) and evaluate the performance of intra-MARIO. The results
highlight that the intra-MARIO reduces overall handoff delay with low power consumption and
minimizes packet losses during handoffs, compared to prior mobility protocols.
IEEE Transactions on Mobile Computing (March 2016
New solution for the creation of MANETs based on personal devices
Abstract - Mobile ad hoc networks (MANETs) enable communication between moving nodes
through multi-hop wireless routes. There are protocols with special features that handle both
auto-configuration and routing in these networks. Nevertheless, many of these auto-configuration
protocols have not been truly implemented and in consequence, only exist a few available
solutions for MANETs conformation. This article presents a new solution for this issue, mainly
in cases where only personal devices are available. The core of the proposal is a new auto-
configuration protocol, which allows dynamic allocation of unique IP addresses and
accomplishes effective answers in front of issues like mergers and partitions of networks, and
others. Finally, the results of simulations in OMNET++ and of tests of a pilot version based on
Android mobile phones are shown.
IEEE Latin America Transactions (April 2016)
Hybrid Spectrum Sharing Through Adaptive Spectrum Handoff and Selection
Abstract - Spectrum sharing is a key function to provide fairness allocation as well as service
satisfaction across multiple users in cognitive radio networks. Even though spectrum sharing can
benefit from spectrum handoff to enhance rate performance by switching from unavailable
channels to available ones, the negative impact on handoff delay can cause significant service
degradation. In this work, we present a hybrid spectrum sharing strategy that includes novel
static and dynamic spectrum sharing algorithms based essentially on a rate compensation
approach and adapted best fit algorithms. The static scheme is applicable for some specific
network configurations where spectrum handoff is not necessary. Conversely, the dynamic

scheme can benefit from spectrum handoff to increase the achieved rate and also compensate for
the lost rate from the unavailable periods. These two sharing schemes are operated adaptively
according to the current network environment. We compare our hybrid strategy with a fully
dynamic one and an optimization framework. The proposed hybrid spectrum sharing
demonstrates its effectiveness in terms of improving the overall service satisfaction and reducing
the number of handoffs while the achieved rate is fulfilling compared to the optimal.
Quantifying Interdependent Privacy Risks with Location Data
Abstract - Co-location information about users is increasingly available online. For instance,
mobile users more and more frequently report their co-locations with other users in the messages
and in the pictures they post on social networking websites by tagging the names of the friends
they are with. The users’ IP addresses also constitute a source of co-location information.
Combined with (possibly obfuscated) location information, such co-locations can be used to
improve the inference of the users’ locations, thus further threatening their location privacy: As
co-location information is taken into account, not only a user’s reported locations and mobility
patterns can be used to localize her, but also those of her friends (and the friends of their friends
and so on). In this paper, we study this problem by quantifying the effect of co-location
information on location privacy, considering an adversary such as a social network operator that
has access to such information. We formalize the problem and derive an optimal inference
algorithm that incorporates such co-location information, yet at the cost of high complexity. We
propose some approximate inference algorithms, including a solution that relies on the belief
propagation algorithm executed on a general Bayesian network model, and we extensively
evaluate their performance. Our experimental results show that, even in the case where the
adversary considers co-locations of the targeted user with a single friend, the median location
privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the
different parameters (e.g., the settings of the location-privacy protection mechanisms) in
different scenarios.

Competition-Based Participant Recruitment for Delay-Sensitive Crowdsourcing
Applications in D2D Networks
Abstract - Device-to-Device (D2D) networks impose a significant challenge on delay-
sensitive crowdsourcing due to the highly nondeterministic and intermittent network
connectivity. Under this setting, the paper investigates a participant recruitment problem in
which an initial set of recruited nodes, which we call seeds, need to make an optimal decision on
what other nodes to recruit to perform the crowdsourcing task. These seeds face the dilemma that
recruiting more nodes increases their own payment but on the other hand also increases the risk
of being excluded from the crowdsourcing task. As a first attack to this problem, we propose a
dynamic programming algorithm. However, it is a centralized solution and hence the practicality
is compromised. Therefore, we introduce two distributed alternatives. One is based on the
divide-and-conquer paradigm by first partitioning a network into a set of opportunistic Voronoi
cells and then running an optimization algorithm in each cell. The other is a task-splitting scheme
which recursively delegates the recruitment task to newly joined nodes. We implemented our
proposed solutions on an Android-based prototype and built a testbed using 25 Dell Streak
tablets. Our experiments which lasted for 24 days demonstrate that the distributed schemes
approximate the theoretical optimum with affordable complexity. Moreover, we conducted
simulations with a much larger scale and more diverse settings. The simulation results
corroborate the experimental data and confirm that our proposed distributed solutions closely
approach the performance of the centralized solution while satisfying the optimization goal under
different network configurations.
IEEE Transactions on Mobile Computing (February 2016
Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in
Cellular Networks
Abstract - In this paper, we present Prius, a hybrid edge cloud and client adaptation
framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities
empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are
capable of accessing application-layer and radio access networks (RAN) information in real
time. Coupled with powerful computation support, an edge cloud assisted strategy is expected to

significantly enrich mobile services. Meanwhile, although HAS has established itself as the
dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular
networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation
under the highly dynamic cellular links. Edge cloud assisted HAS presents a new opportunity to
resolve these issues and achieve systematic enhancement of quality of experience (QoE) and
QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of
adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering
the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN
channel status, client device characteristics as well as applicationlayer information in order to
jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the
cumulative viewing experience and an exponential smoothing estimation to accurately estimate
future channel under different moving patterns. Extensive trace-driven simulation results show
that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast
moving environment. Furthermore, Prius adaptation algorithm achieves a near-optimal
performance that outperforms exiting strategies.
IEEE Transactions on Circuits and Systems for Video Technology (March 2016)
The Design of a Wearable RFID System for Real-time Activity Recognition using Radio
Patterns
Abstract - Elderly care is one of the many applications supported by real-time activity
recognition systems. Traditional approaches use cameras, body sensor networks, or radio
patterns from various sources for activity recognition. However, these approaches are limited due
to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable
Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with
high detection coverage. Our system uses passive tags which are maintenance-free and can be
embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader
is also worn on the user’s body to extend the detection coverage as the user moves. We exploit
RFID radio patterns and extract both spatial and temporal features to characterize various
activities. We also address the issues of false negative of tag readings and tag/antenna
calibration, and design a fast online recognition system. Antenna and tag selection is done
automatically to explore the minimum number of devices required to achieve target accuracy.

We develop a prototype system which consists of a wearable RFID system and a smartphone to
demonstrate the working principles, and conduct experimental studies with four subjects over
two weeks. The results show that our system achieves a high recognition accuracy of 93.6% with
a latency of 5 seconds. Additionally, we show that the system only requires two antennas and
four tagged body parts to achieve a high recognition accuracy of 85%.
A Weighted Crowdsourcing Approach for Network Quality Measurement in Cellular Data
Networks
Abstract - With ubiquitous smartphone usages, it is important for network providers to
provide high-quality service to every user in the network. To make more effective planning and
scheduling, network providers need an accurate estimate of network quality for base stations and
cells from the perspective of user experience. Traditional drive testing approach provides a
quality measurement for each area and the quality measurement is obtained from the equipment
in a moving vehicle. This approach suffers from the limitations of high costs, low coverage, and
out-of-date values. In this paper, we propose a novel crowdsourcing approach for the task of
network quality estimation, which incurs little costs and provides timely and accurate quality
estimation. The proposed approach collects quality measurements from individual end users
within a certain network or cell coverage area, and then aggregates these measurements to obtain
a global measurement of network quality. We propose an effective aggregation scheme which
infers the information weights of end users and incorporates such weights into the estimation of
network quality. Experiments are conducted on two datasets collected from citywide 3G
networks, which involve 616; 796 users and 22; 715 cells. We validate the effectiveness of the
proposed approach compared with baseline method. From the aggregated measurement results,
we observe some interesting patterns about network quality, which can be explained by network
usage and traffic behavior. We also show that proposed approach runs in linear time.
IEEE Transactions on Mobile Computing (March 2016 )
PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in
Mobile Social Networks

Abstract - Mobile social network (MSN) emerges as a promising social network paradigm
that enables mobile users' information sharing in the proximity and facilitates their cyber-
physical-social interactions. As the advertisements, rumors, and spams spread in MSNs, it is
necessary to filter spams before they arrive at the recipients to make the MSN energy efficient.
To this end, we propose a personalized fine-grained filtering scheme (PIF) with privacy
preservation in MSNs. Specifically, we first develop a social-assisted filter distribution scheme,
where the filter creators send filters to their social friends (i.e., filter holders). These filter holders
store filters and decide to block spams or relay the desired packets through coarse-grained and
fine-grained keyword filtering schemes. Meanwhile, the developed cryptographic filtering
schemes protect creator's private information (i.e., keyword) embedded in the filters from
directly disclosing to other users. In addition, we establish a Merkle Hash tree to store filters as
leaf nodes where filter creators can check if the distributed filters need to be updated by
retrieving the value of root node. It is demonstrated that the PIF can protect users' private
keywords included in the filter from disclosure to others and detect forged filters. We also
conduct the trace-driven simulations to show that the PIF can not only filter spams efficiently but
also achieve high delivery ratio and low latency with acceptable resource consumption.
IEEE Transactions on Computational Social Systems (February 2016)
Rethinking Mobile Data Offloading for LTE in Unlicensed Spectrum
Abstract - Traditional mobile data offloading transfers cellular users to WiFi networks to
relieve the cellular system from the pressure of the ever-increasing data traffic load. However,
the spectrum utilization of the WiFi network is bound to suffer from potential packet collisions
due to its contention-based access protocol, especially when the number of competing WiFi users
grows large. To tackle this problem, we propose to transfer some WiFi users to be served by the
LTE system, in contrast to the traditional mobile data offloading which effectively offloads LTE
traffic to the WiFi network. Meanwhile, leveraging the emerging LTE in unlicensed spectrum
(LTE-U) technology, some unlicensed spectrum resources may be allocated to the LTE system in
compensation for handling more WiFi users. In this way, a winwin situation would be generated
since LTE can generally achieve better performance than WiFi due to its capability of centralized
coordination. To facilitate it, three important challenging issues are addressed in the paper:

which WiFi users should be transferred; how many WiFi users need to be transferred; and how
much unlicensed resources should be relinquished to the LTE-U network. We investigate three
different user transfer schemes according to the availability of channel state information (CSI):
the random transfer, the distance-based transfer, and the CSI-based transfer. In each scheme, the
minimum required amount of unlicensed resources under a given transferred user number is
analyzed. Furthermore, we utilize the Nash bargaining solution (NBS) to develop joint user
transfer and unlicensed resource allocation strategy to fulfill the win-win situation for both
networks, whose performance is demonstrated by numerical simulation.
IEEE Transactions on Wireless Communications (April 2016)
E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive
Device-Free Localization
Abstract - Device-free localization (DFL), which does not require any devices being attached
to target(s), has become an appealing technology for many applications, such as intrusion
detection and elderly monitoring. To achieve high localization accuracy, most recent DFL
methods rely on collecting a large number of received signal strength (RSS) changes distorted by
target(s). Consequently, the incurred high energy consumption renders them infeasible for
resource-constraint networks, such as wireless sensor networks. This paper introduces an energy-
efficient framework for high-precision multi-target-adaptive devicefree localization (E-HIPA).
Compared with the existing methods, E-HIPA demands fewer transceivers, applies the
compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change
measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target
locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we
theoretically prove the validity of the proposed CS-based framework problem formulation. Based
on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching
pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with
high probability, even for a more practical scenario with unknown target number. Experimental
results via real testbed demonstrate that, compared with the previous state-of-the-art solutions,
i.e., RTI, SCPL and RASS approaches, E-HIPA reduces the energy consumption by up to 69%
with meter-level localization accuracy.

Chance Constrained Robust Downlink Beamforming in Multicell Networks
Abstract - We introduce a downlink robust optimization approach that minimizes a
combination of total transmit power by a multiple antenna base station (BS) within a cell and the
resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This
optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR)
targets are met at user terminals with certain outage probabilities. The outages are due to the
uncertainties that naturally emerge in the estimation of channel covariance matrices between a
BS and its intra-cell local users as well as the other users of the other cells. We model these
uncertainties using random matrices, analyze their statistical behaviour and formulate a tractable
probabilistic approach to the design of optimal robust downlink beamforming vectors. The
proposed approach reformulates the original intractable non-convex problem in a semidefinite
programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP
formulation is convex and numerically tractable under the standard rank relaxation. We compare
the proposed chance-constrained approach against two different robust design schemes as well as
the worst-case robustness. The simulation results confirm better power efficiency and higher
resilience against channel uncertainties of the proposed approach in realistic scenarios.
Dynamic Frequency Resource Allocation in Heterogeneous Cellular Networks
Abstract - Deployment of low power pico basestations within cellular networks can
potentially increase both capacity and coverage. However, such deployments require efficient
frequency allocation schemes for managing interference from the pico and macro basestations
that are located within each other’s transmission range. Partitioning the available frequencies
between the various basestations avoids the problem of interference, but can lead to inefficient
spectrum usage. In this paper, we introduce a distributed frequency allocation scheme that shares
frequencies between macro and pico basestations, and guarantees a minimum average throughput
to users. The scheme seeks to minimize the total number of frequencies needed to honor the
minimum throughput requirements. We evaluate our scheme using detailed simulations and
show that it performs on par with the centralized optimum allocation. Moreover, our proposed

scheme outperforms a static frequency reuse scheme and the centralized optimal partitioning
between the macro and picos.
Relay-Assisted Device-to-Device Communication: A Stochastic Analysis of Energy Saving
Abstract - This paper lays a mathematical framework for estimating the energy saving of a
relay assisting a pair of wireless devices. We derive closed-form expressions for describing the
geometrical zone where relaying is energy efficient. In addition, we obtain the probabilistic
distribution of the energy saving introduced by relays that are randomly distributed according to
a spatial Poisson point process. Furthermore, we present a comparison methodology for fairly
evaluating the energy consumption of conventional cellular network from one side and relay-
assisted device-to-device communication from another side. Results suggest that a significant
energy saving can be achieved when relay-assisted device-to-device communication is adopted
for distances below a certain threshold. In order to test the analytical framework presented in this
paper, we perform Monte-Carlo simulations and compare the results with those obtained from
the mathematical framework.
Fusion of Modified Bat Algorithm Soft Computing and Dynamic Model Hard Computing
to Online Self-Adaptive Fuzzy Control of Autonomous Mobile Robots
Abstract - This paper presents a fusion methodology of modified bat algorithm (BA) soft
computing and dynamic model hard computing to online self-adaptive fuzzy control of
autonomous mobile robots in a field-programmable gate array (FPGA) chip. This fusion
approach, called fusion of BA fuzzy control (FBAFC), gains the benefits of BA, online fuzzy
control, dynamic model, Taguchi method, and FPGA technique. The FPGA realization of the
proposed FBAFC fusion method is more effective in practice for challenging real-world
embedded applications by using system-on-a-programmable-chip (SoPC) technology.
Experimental results and comparative works are conducted to illustrate the merits of the
proposed SoPC-based FBAFC optimal controller over other existing methods for mobile robots.
IEEE Transactions on Industrial Informatics (June 2016)

Wireless Resource Scheduling Based on Backoff for Multi-user Multi-service Mobile Cloud
Computing
Abstract - Mobile cloud computing (MCC) can significantly improve the processing/storage
capacity and standby time of mobile terminals (MTs) by migrating data processing and storage to
remote cloud. However, due to the wireless resource limitations of access points/base stations,
data streaming of MCC suffers poor quality-of-service (QoS) in multi-user multi-service
scenarios, such as long buffering time and intermittent disruptions. In this paper, we propose a
Backoff based Wireless Resource Scheduling (BWRS) scheme, in which real-time services have
higher priority than non-real-time services. BWRS can improve the QoS of real-time streams and
overall performance of mobile cloud computing networks. We formulate an M/M/1 queueing
model and propose a Queueing-Delay-Optimal Control (QDOC) algorithm to minimize the
average queueing delay of nonreal- time services. Furthermore, a Delay-Constrained Control
(DCC) algorithm is developed not only to minimize the queueing delay of non-real-time services
of muti-service users, but also to support users’ non-real-time services under delay constraints.
The simulation results show that the proposed scheme can minimize the average queueing delay
while still meeting delay requirement, and can significantly improve blocking probability and
channel utilization.
IEEE Transactions on Vehicular Technology (February 2016)
Assessing the Implications of Cellular Network Performance on Mobile Content Access
Abstract - Mobile applications such as VoIP, (live) gaming, or video streaming have diverse
QoS requirements ranging from low delay to high throughput. The optimization of the network
quality experienced by end-users requires detailed knowledge of the expected network
performance. Also, the achieved service quality is affected by a number of factors, including
network operator and available technologies. However, most studies measuring the cellular
network do not consider the performance implications of network configuration and
management. To this end, this paper reports about an extensive data set of cellular network
measurements, focused on analyzing root causes of mobile network performance variability.
Measurements conducted on a 4G cellular network in Germany show that management and
configuration decisions have a substantial impact on the performance. Specifically, it is observed

that the association of mobile devices to a point of presence (PoP) within the operator’s network
can influence the end-to-end performance by a large extent. Given the collected data, a model
predicting the PoP assignment and its resulting RTT leveraging Markov chain and machine
learning approaches is developed. RTT increases of 58% to 73% compared to the optimum
performance are observed in more than 57% of the measurements. Measurements of the response
and page load times of popular websites lead to similar results, namely, a median increase of
40% between the worst and the best performing PoP.
IEEE Transactions on Network and Service Management (June 2016)
Mobile Cloud Support for Semantic-enriched Speech Recognition in Social Care
Abstract - Nowadays, most users carry high computing power mobile devices where speech
recognition is certainly one of the main technologies available in every modern smartphone,
although battery draining and application performance (resource shortage) have a big impact on
the experienced quality. Shifting applications and services to the cloud may help to improve
mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area.
However, the quality of speech recognition is still not sufficient in many complex cases to
replace the common hand written text, especially when prompt reaction to short-term
provisioning requests is required. To address the new scenario, this paper proposes a mobile
cloud infrastructure to support the extraction of semantics information from speech recognition
in the Social Care domain, where carers have to speak about their patients conditions in order to
have reliable notes used afterward to plan the best support. We present not only an architecture
proposal, but also a real prototype that we have deployed and thoroughly assessed with different
queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a
Service (PaaS) testbed based on Cloud Foundry.
IEEE Transactions on Cloud Computing (May 2016)
A User Centered Multi-Objective Handoff Scheme for Hybrid 5G Environments
Abstract - In this paper, we propose a user centered handoff scheme for hybrid 5G
environments. The handoff problem is formulated as a multi-objective optimization problem
which maximizes the achievable data receiving rate and minimizes the block probability
simultaneously. When a user needs to select a new Base Station (BS) in handoff, the user will

calculate the achievable data receiving rate and estimate the block probability for each available
BS based on limited local information. By taking the throughput metric into consideration, the
formulated multi-objective optimization problem is then transformed into a maximization
problem. We solve the transformed maximization problem to calculate the network selection
result in a distributed method. The calculated network selection result is proved to be a Pareto
Optimal solution of the original multi-objective optimization problem. The proposed scheme
guarantees that based on limited local information, each user can select a new BS with high
achievable data receiving rate and low block probability in handoff. Comprehensive experiment
has been conducted. It is shown that the proposed scheme promotes the total throughput and ratio
of users served significantly.
IEEE Transactions on Emerging Topics in Computing (April 2016)
Robust Sparse Coding for Mobile Image Labeling on the Cloud
Abstract - With the rapid development of the mobile service and online social networking
service, a large of mobile images are generated and shared on the social networks every day. The
visual content of these images contains rich knowledge for many uses, such as social
categorization and recommendation. Mobile image labeling has therefore been proposed to
understand the visual content and received intensive attention in recent years. In this paper, we
present a novel mobile image labeling scheme on the cloud, in which mobile images are first and
efficiently transmitted to the cloud by Hamming compressed sensing (HCS) such that the heavy
computation for image understanding is transferred to the cloud for fast response to the queries
of users. On the cloud, we design a sparse correntropy framework for robustly learning the
semantic content of mobile images, based on which the relevant tags are assigned to the query
images. The proposed framework (called McMil) is very insensitive to noise and outliers, and is
optimized by a half-quadratic optimization technique. We theoretically show that our image
labeling approach is more robust than the squared loss, absolute loss, Cauchy loss and many
other robust loss function based sparse coding methods. To further understand the proposed
algorithm, we also derive its robustness and generalization error bounds. At last, we conduct
experiments on the PASCAL VOC’07 dataset and empirically demonstrate the effectiveness of
the proposed robust sparse coding method for mobile image labeling.
IEEE Transactions on Circuits and Systems for Video Technology (March 2016)

Optimal Resource Sharing in 5G-enabled Vehicular Networks: A Matrix Game Approach
Abstract - Vehicular networks are expected to accommodate a large number of data-heavy
mobile devices and multi-application services. Whereas, it faces a significant challenge when we
need to deal with the ever-increasing demand of mobile traffic. In this paper, we present a new
paradigm of 5G-enabled vehicular networks to improve the network capacity and the system
computing capability. We extend the original cloud radio access network (C-RAN) to integrate
local cloud services to provide a low-cost, scalable, self-organizing and effective solution. The
new C-RAN is named as Enhanced C-RAN (EC-RAN). Cloudlets in EC-RAN are
geographically distributed for local services. Furthermore, Device-to-Device (D2D) and
Heterogeneous Networks (HetNet) are essential technologies in 5G systems. They can greatly
improve the spectrum efficiency and support the large-scale live video streaming in short-
distance communications. We exploit the matrix game theoretical approach to operate the
cloudlet resource management and allocation. Nash equilibrium solution can be obtained by
Karush-Kuhn-Tucker nonlinear complementarity approach. Illustrative results indicate that the
proposed resource sharing scheme with the geo-distributed cloudlets can improve the resource
utilization and reduce the system power consumption. Moreover, with the integration of the
Softwaredefine network (SDN) architecture, a vehicular network can easily reach a globally
optimal solution.
IEEE Transactions on Vehicular Technology (March 2016)
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