Ns2 2015 2016 titles abstract

Joint Access Control and Resource Allocation for Concurrent and Massive Access of
M2M Devices
Machine-to-machine (M2M) communications, also known as machine-type communications
(MTC) in 3GPP LTE systems, provide autonomous connectivity between machines without
human intervention to create new service, e.g., the Internet of Things and the smart grid. M2M
communications normally involve a large number of MTC devices (MTCDs) to support a variety
of sensor applications. Consequently, concurrent and massive access attempts of MTCDs to
radio access networks (RANs) may cause intolerable delay, packet loss, and even service
unavailability. In this paper, we propose a joint optimal physical random access channel
(PRACH) resource allocation and access control mechanism to address the performance
degradation caused by concurrent and massive access attempts of MTCDs in LTE systems. We
define the notion of random access efficiency and formulate an optimization problem for
maximization of the random access efficiency with random access delay constraint. We also
propose a dynamic resource allocation and access control algorithm based on estimation of the
number of MTCDs for a system with dynamically varying numbers of massive MTCDs. Then,
an analytical model is provided using a discrete-time Markov chain for the proposed mechanism.
The effectiveness of the proposed algorithm is demonstrated via analysis and simulations. The
proposed algorithm was able to maintain the optimal random access efficiency while satisfying
the average random access delay requirement of MTCDs in order to handle massive and dynamic
MTCDs per cell.
Downlink Power Control in Self-Organizing Dense Small Cells Underlaying Macrocells: A
Mean Field Game
A novel distributed power control paradigm is proposed for dense small cell networks co-
existing with a traditional macrocellular network. The power control problem is first modeled as
a stochastic game and the existence of the Nash Equilibrium is proven. Then we extend the
formulated stochastic game to a mean field game (MFG) considering a highly dense network. An
MFG is a special type of differential game which is ideal for modeling the interactions among a

large number of entities. We analyze the performance of two different cost functions for the
mean field game formulation. Both of these cost functions are designed using stochastic
geometry analysis in such a way that the cost functions are valid for the MFG setting. A finite
difference algorithm is then developed based on the Lax-Friedrichs scheme and Lagrange
relaxation to solve the corresponding MFG. Each small cell base station can independently
execute the proposed algorithm offline, i.e., prior to data transmission. The output of the
algorithm shows how each small cell base station should adjust its transmit power in order to
minimize the cost over a predefined period of time. Moreover, sufficient conditions for the
uniqueness of the mean field equilibrium for a generic cost function are also given. The
effectiveness of the proposed algorithm is demonstrated via numerical results.
Hybrid Opportunistic Relaying and Jamming With Power Allocation for Secure
Cooperative Networks
This paper studies the cooperative transmission for securing a decode-and-forward (DF) two-hop
network where multiple cooperative nodes coexist with a potential eavesdropper. Under the more
practical assumption that only the channel distribution information (CDI) of the eavesdropper is
known, we propose an opportunistic relaying with artificial jamming secrecy scheme, where a
“best” cooperative node is chosen among a collection of N possible candidates to forward the
confidential signal and the others send jamming signals to confuse the eavesdroppers. We first
investigate the ergodic secrecy rate (ESR) maximization problem by optimizing the power
allocation between the confidential signal and jamming signals. In particular, we exploit the
limiting distribution technique of extreme order statistics to build an asymptotic closed-form
expression of the achievable ESR and the power allocation is optimized to maximize the ESR
lower bound. Although the optimization problems are non-convex, we propose a sequential
parametric convex approximation (SPCA) algorithm to locate the Karush-Kuhn-Tucker (KKT)
solutions. Furthermore, taking the time variance of the legitimate links' CSIs into consideration,
we address the impacts of the outdated CSIs to the proposed secrecy scheme, and derive an
asymptotic ESR. Finally, we generalize the analysis to the scenario with multiple eavesdroppers,
and give the asymptotic analytical results of the achievable ESR. Simulation results confirm our
analytical results.

Assessing Performance Gains Through Global Resource Control of Heterogeneous
Wireless Networks
We study the resource allocation and management issues related to heterogeneous wireless
systems made up of several Radio Access Technologies (RATs) that collectively provide a
unified wireless network to a diverse set of users through co-ordination managed by a centralized
Global Resource Controller (GRC). We assume that the user devices are multimodal, which
makes it possible for each device to use any available Access Point (AP)/Base Station (BS) of a
RAT at any given time. Through detailed protocol level simulations performed in ns-2, we show
an increase in spectral efficiency of up to 99% and an increase in short-term fairness of up to
28.5% for two greedy sort-based user device-to-AP/BS association algorithms implemented at
the GRC compared to a distributed solution used in practice today where each user makes his/her
own association decision. While the increase in overhead due to re-associations for a centralized
solution grows only slightly (by up to 4.1%) compared to a distributed solution, we find the
performance increase in spectral efficiency and short-term fairness attributes come at the cost of
an order of magnitude increase (of up to 794%) in energy consumption.
Greening Geographical Load Balancing
Energy expenditure has become a significant fraction of data center operating costs. Recently,
“geographical load balancing” has been proposed to reduce energy cost by exploiting the
electricity price differences across regions. However, this reduction of cost can paradoxically
increase total energy use. We explore whether the geographical diversity of Internet-scale
systems can also provide environmental gains. Specifically, we explore whether geographical
load balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil
fuel energy. We make two contributions. First, we derive three distributed algorithms for
achieving optimal geographical load balancing. Second, we show that if the price of electricity is
proportional to the instantaneous fraction of the total energy that is brown, then geographical
load balancing significantly reduces brown energy use. However, the benefits depend strongly on
dynamic energy pricing and the form of pricing used.

A Hierarchical Account-Aided Reputation Management System for MANETs
Encouraging cooperative and deterring selfish behaviors are important for proper operations of
MANETs. For this purpose, most previous efforts either rely on reputation systems or price
systems. However, both systems are neither sufficiently effective in providing cooperation
incentives nor efficient in resource consumption. Nodes in both systems can be uncooperative
while still being considered trustworthy. Also, information exchange between mobile nodes in
reputation systems and credit circulation in price systems consume significant resources. This
paper presents a hierarchical Account-aided Reputation Management system (ARM) to
efficiently and effectively provide cooperation incentives. ARM builds a hierarchical locality-
aware DHT infrastructure for efficient and integrated operations of both reputation and price
systems. The infrastructure helps to globally collect all reputation information in the system,
which helps to calculate more accurate reputation and detect abnormal reputation information.
Also, ARM coordinately integrates resource and price systems by enabling higher-reputed nodes
to pay less for their received services. Theoretical analysis demonstrates the properties of ARM.
Simulation results show that ARM outperforms both a reputation system and price system in
terms of effectiveness and efficiency.
Utility Fair Optimization of Antenna Tilt Angles in LTE Net works
We formulate adaptation of antenna tilt angle as a utility fair optimization task. This optimization
problem is nonconvex, but in this paper we show that, under reasonable conditions, it can be
reformulated as a convex optimization. Using this insight, we develop a lightweight method for
finding the optimal antenna tilt angles, making use of measurements that are already available at
base stations, and suited to distributed implementation.

Efficient Allocation Of Periodic Feedback Channels In Broadband Wireless Networks
Advanced wireless technologies such as multiple-input–multiple-output (MIMO) require each
mobile station (MS) to send a lot of feedback to the base station. This periodic feedback
consumes much of the uplink bandwidth. This expensive bandwidth is very often viewed as a
major obstacle to the deployment of MIMO and other advanced closed-loop wireless
technologies. This paper is the first to propose a framework for efficient allocation of periodic
feedback channels to the nodes of a wireless network. Several relevant optimization problems are
defined and efficient algorithms for solving them are presented. A scheme for deciding when the
base station (BS) should invoke each algorithm is also proposed and shown through simulations
to perform very well.
Proportional Fair Coding For wireless mesh Networks
We consider multihop wireless networks carrying unicast flows for multiple users. Each flow has
a specified delay deadline, and the lossy wireless links are modeled as binary symmetric
channels (BSCs). Since transmission time, also called airtime, on the links is shared among
flows, increasing the airtime for one flow comes at the cost of reducing the airtime available to
other flows sharing the same link. We derive the joint allocation of flow airtimes and coding
rates that achieves the proportionally fair throughput allocation. This utility optimization
problem is nonconvex, and one of the technical contributions of this paper is to show that the
proportional fair utility optimization can nevertheless be decomposed into a sequence of convex
optimization problems. The solution to this sequence of convex problems is the unique solution
to the original nonconvex optimization. Surprisingly, this solution can be written in an explicit
form that yields considerable insight into the nature of the proportional fair joint airtime/coding
rate allocation. To our knowledge, this is the first time that the utility fair joint allocation of
airtime/coding rate has been analyzed, and also one of the first times that utility fairness with
delay deadlines has been considered.

Exploiting Asynchronous Amplify-And-Forward Relays To Enhance The Performance Of
Ieee 802.11 Networks
Cooperative communication is a promising path to recover from performance anomaly in IEEE
802.11 networks. However, a simple solution for employing multiple relays to enhance the relay
link quality has not been proposed. The main obstacle for multiple relay utilization in distributed
networks is that synchronizing relay transmissions requires huge signaling overhead. In this
paper, we investigate the problem from both a physical-layer and MAC-layer point of view. In
the physical layer, a simple, practical solution that provides diversity gain from asynchronous
relay transmissions is introduced. In the MAC layer, a rate adaptation algorithm, RA-ARF, that
takes the extra relay path into account is discussed, and R-MAC is designed to utilize relays in
IEEE 802.11 networks. Our simulation results show considerable improvement in network
performance using R-MAC.
Cellular Architecture And Key Technologies For 5g Wireless Communication Networks
The fourth generation wireless communication systems have been deployed or are soon to be
deployed in many countries. However, with an explosion of wireless mobile devices and
services, there are still some challenges that cannot be accommodated even by 4G, such as the
spectrum crisis and high energy consumption. Wireless system designers have been facing the
continuously increasing demand for high data rates and mobility required by new wireless
applications and therefore have started research on fifth generation wireless systems that are
expected to be deployed beyond 2020. In this article, we propose a potential cellular architecture
that separates indoor and outdoor scenarios, and discuss various promising technologies for 5G
wireless communication systems, such as massive MIMO, energy-efficient communications,
cognitive radio networks, and visible light communications. Future challenges facing these
potential technologies are also discussed.

Energy-Efficient Sensor Scheduling Algorithm in Cognitive Radio Networks Employing
Heterogeneous Sensors
We consider, in this paper, the maximization of throughput in a dense network of collaborative
cognitive radio (CR) sensors with limited energy supply. In our case, the sensors are mixed
varieties (heterogeneous) and are battery powered. We propose an ant colony-based energy-
efficient sensor scheduling algorithm (ACO-ESSP) to optimally schedule the activities of the
sensors to provide the required sensing performance and increase the overall secondary system
throughput. The proposed algorithm is an improved version of the conventional ant colony
optimization (ACO) algorithm, specifically tailored to the formulated sensor scheduling problem.
We also use a more realistic sensor energy consumption model and consider CR networks
employing heterogeneous sensors (CRNHSs). Simulations demonstrate that our approach
improves the system throughput efficiently and effectively compared with other algorithms.
Opportunistic Spectrum Access with Two Channel Sensing in Cognitive Radio Networks
14 Optimal Scheduling for Multi-Radio Multi-Channel Multi-Hop Cognitive Cellular
Networks
In cognitive radio networks, spectrum sensing is a critical to both protecting the primary users
and creating spectrum access opportunities of secondary users. Channel sensing itself, including
active probing and passive listening, often incurs cost, in terms of time overhead, energy
consumption, or intrusion to primary users. It is thus not desirable to sense the channel
arbitrarily. In this paper, we are motivated to consider the following problem. A secondary user,
equipped with spectrum sensors, dynamically accesses a channel. If it transmits without/with
colliding with primary users, a certain reward/penalty is obtained. If it senses the channel,
accurate channel information is obtained, but a given channel sensing cost incurs. The third
option for the user is to turn off the sensor/transmitter and go to sleep mode, where no cost/gain
incurs. So when should the secondary user transmit, sense, or sleep, to maximize the total gain?
We derive the optimal transmitting, sensing, and sleeping structure, which is a threshold-based
policy. Our work sheds light on designing sensing and transmitting scheduling protocols for
cognitive radio networks, especially the in-band sensing mechanism in 802.22 networks.

DSCA: Dynamic Spectrum Co-Access Between the Primary Users and the Secondary Users
In the current architecture of dynamic spectrum access, secondary users (SUs) only
opportunistically access the spectrum of primary users (PUs). The resurgence of PUs disrupts
secondary communications, which can result in poor performance for SUs. In this paper, we
propose a novel architecture for dynamic spectrum access, termed dynamic spectrum co-access
(DSCA), to enable the PU and the SU to simultaneously access the licensed spectrum. With
DSCA, SUs transparently incentivize PUs through increasing the PU performance so that SUs
can access the spectrum simultaneously with PUs; hence, there is no disruption to secondary
communications due to the resurgence of PUs. We derive a mathematical model to formulate the
minimum incentives for the spectrum co-access between the PU and the SU and to compute the
region of co-access to determine the SUs that can co-access with a given PU. An algorithm is
also developed to select the co-access primary and secondary links to maximize network
performance. Numerical results indicate that DSCA significantly improves performance
compared with the current architecture of dynamic spectrum access.
On Joint Power and Admission Control in Underlay Cellular Cognitive Radio Networks
We investigate the problem of designing efficient and low-complexity centralized algorithms for
joint power and admission control in a cellular cognitive radio network (CRN) which coexists
with a primary radio network (PRN) in a spectrum underlay fashion. We first derive a simple
one-to-one relation between the signal-to-interference-plus-noise ratio (SINR) vector and its
corresponding power vector of all users of the CRN and PRN, and based on this we propose two
new admission metrics. Then, in an infeasible system, where the minimum acceptable target-
SINRs for all primary and secondary users are not simultaneously reachable, two centralized
algorithms are proposed. These algorithms aim at removing the minimal number of secondary
users (based on the proposed admission metrics), subject to the constraint that all primary users
are supported with their target-SINRs. In an infeasible system, our proposed algorithms

outperform other existing algorithms in terms of complexity and secondary users' outage ratio.
Furthermore, for a feasible system, where all secondary users can be admitted along with all
primary users, by using our derived one-to-one relation between SINR and power vector, we
solve the problems of maximizing aggregate throughput and max-min quality-of-service (QoS)
for secondary users, both subject to the constraint that all primary and secondary users are
supported with their minimum target-SINRs.
Cooperative Relay Selection in Cognitive Radio Networks
Cognitive radio has been proposed in recent years to promote the spectrum utilization by
exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and
traffic demand in secondary users has brought significant challenge for efficient spectrum
allocation in cognitive radio networks. Observing that spectrum resource can be better matched
to traffic demand of secondary users with the help of relay node that has rich spectrum resource,
in this paper we exploit a new research direction for cognitive radio networks by utilizing
cooperative relay to assist the transmission and improve spectrum efficiency. An infrastructure-
based secondary network architecture has been proposed to leverage relay-assisted discontiguous
OFDM (D-OFDM) for data transmission. In this architecture, relay node will be selected which
can bridge the source and the destination using its common channels between those two nodes.
With the introduction of cooperative relay, many unique problems should be considered,
especially the issue for relay selection and spectrum allocation. We propose a centralized
heuristic solution to address the new resource allocation problem. To demonstrate the feasibility
and performance of cooperative relay for cognitive radio, a new MAC protocol has been
proposed and implemented in a Universal Software Radio Peripheral (USRP)-based testbed.
Experimental results show that the throughput of the whole system is greatly increased by
exploiting the benefit of cooperative relay.

Statistical Modeling of Spectrum Sensing Energy in Multi-Hop Cognitive Radio Networks
The aim of this letter is to address the statistical modeling of the spectrum sensing energy
consumption in cognitive radio networks. A Poisson point process has been shown to yield
tractable and accurate results for the modeling of the interference in cognitive radio networks.
We adopt this homogeneous stochastic process to develop an unified framework for deriving the
energy consumption of the spectrum sensing in clustered cognitive radio networks. Furthermore,
we extend the framework to multi-hop networks. The letter demonstrates that the spectrum
sensing energy can be modeled as a Gamma-truncated distribution, as a function of the number
of secondary users, their spatial density, and the number of hops of the cognitive radio network.
Adaptive Random Access for Cooperative Spectrum Sensing in Cognitive Radio Networks
In this paper, an adaptive cooperative spectrum sensing scheme using random access is proposed
in a cognitive radio network. Although cooperative spectrum sensing improves the performance
of spectrum sensing considerably, the problem of how to collect sensing data should be solved
for the implementation of the cooperative sensing. This is not an easy problem because
complicated coordination between secondary users paprticipating in the cooperative sensing is
required. This study addresses this problem in an environment of unequal SNR values of primary
user signal at the secondary users. In the proposed scheme, random access is used to collect the
spectrum sensing data of the secondary users during collection period and the length of the
collection period is determined adaptively based on the sensing data collected so far. Thus,
complex slot management for the collection of the sensing data is not necessary. This adaptive
determination of the collection period is formulated as a finite-horizon decision problem.
Backward induction approach is employed to decide the optimal stopping time of the collection
period. In addition, a heuristic algorithm is proposed, which almost equals the performance of
the backward induction method and whose time complexity is much smaller than the backward
induction scheme. Analysis of the proposed scheme is also provided. Numerical results show
that the proposed scheme performs much better than other conventional methods.

Robust Power Control Under Location and Channel Uncertainty in Cognitive Radio
Networks
In this letter, we consider the power control problem in cognitive radio (CR) networks when both
primary user (PU) location and wireless channel are unknown. Prior work in power control
assumes perfect knowledge of PU and CR locations, which is not practical due to localization
errors and node mobility. We assume the distance estimation error in CR-PU links to model
location uncertainties and derive the distribution of channel gain with distance-dependent path
loss and shadowing. We then proceed to develop an optimization framework for CR power
control, which maximizes the CR data rate under PU interference power constraint. Simulation
results showing the CR data rate and interference probability to the PUs are presented to
demonstrate the superior performance of the proposed algorithm compared with reference
schemes.
Energy Efficient Collaborative Spectrum Sensing Based on Trust Management in
Cognitive Radio Networks
An energy efficient collaborative spectrum sensing (EE-CSS) protocol, based on trust
management, is proposed. The protocol achieves energy efficiency by reducing the total number
of sensing reports exchanged between the honest secondary users (HSUs) and the secondary user
base station (SUBS) in a traditional collaborative spectrum sensing (T-CSS) protocol. It is shown
that the minimum total number of sensing reports required to satisfy a target global false alarm
(FA) and missed detection (MD) probabilities in T-CSS is higher than that in EE-CSS.
Expressions for the steady-state average SU trust value τ̅ and total number N̅ of SU sensing
reports transmitted are derived, as is an expression for the energy consumption, in EE-CSS and
T-CSS. The global FA and detection probabilities Qf and Qd are obtained for a commonly used
decision fusion technique. The impact of link outages on τ̅, N̅ , Qf, and Qd is also analyzed. The
results show that the energy consumption in EE-CSS can be much lower compared to that in T-
CSS for long range communications where the transmit energy is dominant.

Improving the Network Lifetime of MANETs through Cooperative MAC Protocol Design
Cooperative communication, which utilizes nearby terminals to relay the overhearing
information to achieve the diversity gains, has a great potential to improve the transmitting
efficiency in wireless networks. To deal with the complicated medium access interactions
induced by relaying and leverage the benefits of such cooperation, an efficient Cooperative
Medium Access Control (CMAC) protocol is needed. In this paper, we propose a novel cross-
layer Distributed Energy-adaptive Location-based CMAC protocol, namely DEL-CMAC, for
Mobile Ad-hoc NETworks (MANETs). The design objective of DEL-CMAC is to improve the
performance of the MANETs in terms of network lifetime and energy efficiency. A practical
energy consumption model is utilized in this paper, which takes the energy consumption on both
transceiver circuitry and transmit amplifier into account. A distributed utility-based best relay
selection strategy is incorporated, which selects the best relay based on location information and
residual energy. Furthermore, with the purpose of enhancing the spatial reuse, an innovative
network allocation vector setting is provided to deal with the varying transmitting power of the
source and relay terminals. We show that the proposed DEL-CMAC significantly prolongs the
network lifetime under various circumstances even for high circuitry energy consumption cases
by comprehensive simulation study.
Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative
Bait Detection Approach
In mobile ad hoc networks (MANETs), a primary requirement for the establishment of
communication among nodes is that nodes should cooperate with each other. In the presence of
malevolent nodes, this requirement may lead to serious security concerns; for instance, such
nodes may disrupt the routing process. In this context, preventing or detecting malicious nodes
launching grayhole or collaborative blackhole attacks is a challenge. This paper attempts to

resolve this issue by designing a dynamic source routing (DSR)-based routing mechanism, which
is referred to as the cooperative bait detection scheme (CBDS), that integrates the advantages of
both proactive and reactive defense architectures. Our CBDS method implements a reverse
tracing technique to help in achieving the stated goal. Simulation results are provided, showing
that in the presence of malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-
effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet
delivery ratio and routing overhead (chosen as performance metrics).
Joint Optimal Data Rate and Power Allocation in Lossy Mobile Ad Hoc Networks with
Delay-Constrained Traffics So
The emerging wireless energy transfer technology enables charging sensor batteries in a wireless
sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough
in this area has opened up a new dimension to the design of sensor network protocols. In the
meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying
in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks
imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we
propose a framework of joint wireless energy replenishment and anchor-point based mobile data
gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-
varying nature of energy replenishment. To that end, we first determine the anchor point
selection strategy and the sequence to visit the anchor points. We then formulate the WerMDG
problem into a network utility maximization problem which is constrained by flow, energy
balance, link and battery capacity and the bounded sojourn time of the mobile collector.
Furthermore, we present a distributed algorithm composed of cross-layer data control,
scheduling and routing subalgorithms for each sensor node, and sojourn time allocation
subalgorithm for the mobile collector at different anchor points. We also provide the
convergence analysis of these subalgorithms. Finally, we implement the WerMDG algorithm in a
distributed manner in the NS-2 simulator and give extensive numerical results to verify the
convergence of the proposed algorithm and the impact of utility weight, link capacity and
recharging rate on network performance.

Mobile-Projected Trajectory Algorithm With Velocity-Change Detection for Predicting
Residual Link Lifetime in MANET
We study the estimation of residual link lifetime (RLL) in mobile ad hoc networks (MANETs)
using the distances between the link's nodes. We first prove that to compute uniquely the RLL, at
least four distance measurements are required. We also demonstrate that random measurement
errors are the dominant factor in prediction inaccuracy and that systematic errors are negligible.
We then propose a mobile-projected trajectory (MPT) algorithm, which estimates the relative
trajectory between two nodes from periodical measurements of the distances between them.
Using the relative trajectory, the algorithm estimates the RLL of the link between the two nodes.
For comparison purposes, we derive a theoretical upper bound on the achievable prediction
inaccuracy by any distance-based RLL prediction algorithm with unknown but finitely bounded
measurement-error distribution. To account for velocity changes, the MPT is enhanced with a
velocity-change detection (VCD) test. Performance evaluation demonstrates robustness in RLL
prediction for piecewise-linear trajectory and multiple velocity changes during the link lifetime.
BRACER: A Distributed Broadcast Protocol in Multi-Hop Cognitive Radio Ad Hoc
Networks with Collision Avoidance
Broadcast is an important operation in wireless ad hoc networks where control information is
usually propagated as broadcasts for the realization of most networking protocols. In traditional
ad hoc networks, since the spectrum availability is uniform, broadcasts are delivered via a
common channel which can be heard by all users in a network. However, in cognitive radio (CR)
ad hoc networks, different unlicensed users may acquire different available channel sets. This
non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad

hoc networks. In this paper, a fully-distributed Broadcast protocol in multi-hop Cognitive Radio
ad hoc networks with collision avoidance, BRACER, is proposed. In our design, we consider
practical scenarios that each unlicensed user is not assumed to be aware of the global network
topology, the spectrum availability information of other users, and time synchronization
information. By intelligently downsizing the original available channel set and designing the
broadcasting sequences and scheduling schemes, our proposed broadcast protocol can provide
very high successful broadcast ratio while achieving very short average broadcast delay. It can
also avoid broadcast collisions. To the best of our knowledge, this is the first work that addresses
the unique broadcasting challenges in multi-hop CR ad hoc networks with collision avoidance.
Interference-Based Topology Control Algorithm for Delay-Constrained Mobile Ad Hoc
Networks
As the foundation of routing, topology control should minimize the interference among nodes,
and increase the network capacity. With the development of mobile ad hoc networks (MANETs),
there is a growing requirement of quality of service (QoS) in terms of delay. In order to meet the
delay requirement, it is important to consider topology control in delay constrained environment,
which is contradictory to the objective of minimizing interference. In this paper, we focus on the
delay-constrained topology control problem, and take into account delay and interference jointly.
We propose a cross-layer distributed algorithm called interference-based topology control
algorithm for delay-constrained (ITCD) MANETs with considering both the interference
constraint and the delay constraint, which is different from the previous work. The transmission
delay, contention delay and the queuing delay are taken into account in the proposed algorithm.
Moreover, the impact of node mobility on the interference-based topology control algorithm is
investigated and the unstable links are removed from the topology. The simulation results show
that ITCD can reduce the delay and improve the performance effectively in delay-constrained
mobile ad hoc networks.

Cooperative Load Balancing and Dynamic Channel Allocation for Cluster-Based Mobile
Ad Hoc Networks
Mobile ad hoc networks (MANETs) are becoming increasingly common, and typical network
loads considered for MANETs are increasing as applications evolve. This, in turn, increases the
importance of bandwidth efficiency while maintaining tight requirements on energy
consumption, delay and jitter. Coordinated channel access protocols have been shown to be well
suited for highly loaded MANETs under uniform load distributions. However, these protocols
are in general not as well suited for non-uniform load distributions as uncoordinated channel
access protocols due to the lack of on-demand dynamic channel allocation mechanisms that exist
in infrastructure based coordinated protocols. In this paper, we present a lightweight dynamic
channel allocation mechanism and a cooperative load balancing strategy that are applicable to
cluster based MANETs to address this problem. We present protocols that utilize these
mechanisms to improve performance in terms of throughput, energy consumption and inter-
packet delay variation (IPDV). Through extensive simulations we show that both dynamic
channel allocation and cooperative load balancing improve the bandwidth efficiency under non-
uniform load distributions compared to protocols that do not use these mechanisms as well as
compared to the IEEE 802.15.4 protocol with GTS mechanism and the IEEE 802.11
uncoordinated protocol.
CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes
Mobile ad-hoc networks (MANETs) assume that mobile nodes voluntary cooperate in order to
work properly. This cooperation is a cost-intensive activity and some nodes can refuse to
cooperate, leading to a selfish node behaviour. Thus, the overall network performance could be

seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes.
However, the detection process performed by watchdogs can fail, generating false positives and
false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone
can lead to poor performance when detecting selfish nodes, in term of precision and speed. This
is specially important on networks with sporadic contacts, such as delay tolerant networks
(DTNs), where sometimes watchdogs lack of enough time or information to detect the selfish
nodes. Thus, we propose collaborative contact-based watchdog (CoCoWa) as a collaborative
approach based on the diffusion of local selfish nodes awareness when a contact occurs, so that
information about selfish nodes is quickly propagated. As shown in the paper, this collaborative
approach reduces the time and increases the precision when detecting selfish nodes.
Estimating the Available Medium Access Bandwidth of IEEE 802.11 Ad Hoc Networks
with Concurrent Transmissions
Concurrent transmission scheduling mechanisms can significantly improve the total throughput
of IEEE 802.11 ad hoc networks. What remains unaddressed, however, is how to estimate the
available medium access bandwidth (AB) of a link under concurrent transmission scenarios, i.e.,
the maximum throughput that can be obtained without violating the quality-of-service (QoS)
requirements of the existing flows. In this paper, we focus on estimating the available medium
AB of IEEE 802.11 ad hoc networks with the control-gap-based concurrent transmissions. We
first introduce the upper bound of the AB of a link, and then, we present an algorithm to estimate
the available transmission/reception duration of the node by a distributed manner. We further
derive a preliminary estimation of the AB by analyzing the nonoverlap between the medium
availability of the sender and recipient of a link. Finally, we refine the estimation by taking into
account the transmission failures induced by hidden nodes and concurrent collisions. Extensive
simulations demonstrate the accuracy of the proposed approach.

Dynamic Channel Assignment for Wireless Sensor Networks: A Regret Matching Based
Approach
Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel
transmission and to reduce interference. However, the extra overhead posed by the multi-channel
usage coordination dramatically challenges the energy-constrained WSNs. In this paper, we
propose a Regret Matching based Channel Assignment algorithm (RMCA) to address this
challenge, in which each sensor node updates its choice of channels according to the historical
record of these channels' performance to reduce interference. The advantage of RMCA is that it
is highly distributed and requires very limited information exchange among sensor nodes. It is
proved that RMCA converges almost surely to the set of correlated equilibrium. Moreover,
RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and
network topology. Simulations show that RMCA achieves better network performance in terms
of both delivery ratio and packet latency than CONTROL [1], MMSN [2] and randomized
CSMA. In addition, real hardware experiments are conducted to demonstrate that RMCA is easy
to be implemented and performs better.
Segment-Based Anomaly Detection with Approximated Sample Covariance Matrix in
Wireless Sensor Networks
In wireless sensor networks (WSNs), it has been observed that most abnormal events persist over
a considerable period of time instead of being transient. As existing anomaly detection
techniques usually operate in a point-based manner that handles each observation individually,
they are unable to reliably and efficiently report such long-term anomalies appeared in an
individual sensor node. Therefore, in this paper, we focus on a new technique for handling data
in a segment-based manner. Considering a collection of neighbouring data segments as random
variables, we determine those behaving abnormally by exploiting their spatial predictabilities
and, motivated by spatial analysis, specifically investigate how to implement a prediction

variance detector in a WSN. As the communication cost incurred in aggregating a covariance
matrix is finally optimised using the Spearman's rank correlation coefficient and differential
compression, the proposed scheme is able to efficiently detect a wide range of long-term
anomalies. In theory, comparing to the regular centralised approach, it can reduce the
communication cost by approximately 80 percent. Moreover, its effectiveness is demonstrated by
the numerical experiments, with a real world data set collected by the Intel Berkeley Research
Lab (IBRL).
Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless
Sensor Networks
Target coverage and data collection are two fundamental problems for wireless sensor networks
(WSNs). Target coverage is needed to select sensors in a given area that can monitor a set of
interesting points. Data collection is needed to transmit the sensed data from sensors to a sink.
Since, in many applications, sensors are battery powered, it is expected that a WSN can work
untended for a long period. This paper addresses the scheduling problems for both target
coverage and data collection in WSNs with the objective of maximizing network lifetime. First, a
polynomial-time approximation scheme is developed for the case where the density of target
points is bounded, and then, a polynomial-time constant-factor approximation algorithm is
developed for the general case. It is also proved that it is NP-hard to find a maximum lifetime
scheduling of target cover and data collection for a WSN, even if all the sensors have the same
sensing radius and the same transmission radius. Further, the practical efficiency of our
algorithms is analyzed through simulation. These extensive simulation results show better
performances of our algorithms compared with other research findings.

Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in
Wireless Sensor Networks
In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor
networks, which includes the sensor layer, cluster head layer, and mobile collector (called
SenCar) layer. The framework employs distributed load balanced clustering and dual data
uploading, which is referred to as LBC-DDU. The objective is to achieve good scalability, long
network lifetime and low data collection latency. At the sensor layer, a distributed load balanced
clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In
contrast to existing clustering methods, our scheme generates multiple cluster heads in each
cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the
inter-cluster transmission range is carefully chosen to guarantee the connectivity among the
clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-
saving inter-cluster communications. Through inter-cluster transmissions, cluster head
information is forwarded to SenCar for its moving trajectory planning. At the mobile collector
layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously
upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output
(MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual
data uploading capability by properly selecting polling points in each cluster. By visiting each
selected polling point, SenCar can efficiently gather data from cluster heads and transport the
data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of
the proposed LBC-DDU scheme. The results show that when each cluster has at most two cluster
heads, LBC-DDU achieves over 50 percent energy saving per node and 60 percent energy saving
on cluster heads comparing with data collection through multi-hop relay to the static data sink,
and 20 percent - horter data collection time compared to traditional mobile data gathering.

Joint Cooperative Routing and Power Allocation for Collision Minimization in Wireless
Sensor Networks With Multiple Flows
In this letter, a cross-layer cooperative routing algorithm is proposed for minimizing the collision
probability subject to an end-to-end outage probability constraint. We develop a collision
minimization algorithm by combining cooperative transmission, optimal power allocation, and
route selection. The proposed cooperative routing algorithm, called minimum collision
cooperative routing (MCCR), selects the route that causes minimum collision probability to other
nodes in the network. Results show that MCCR can significantly reduce the collision probability
compared with existing cooperative routing schemes.
PWDGR: Pair-Wise Directional Geographical Routing Based on Wireless Sensor Network
Multipath routing in wireless multimedia sensor network makes it possible to transfer data
simultaneously so as to reduce delay and congestion and it is worth researching. However, the
current multipath routing strategy may cause problem that the node energy near sink becomes
obviously higher than other nodes which makes the network invalid and dead. It also has serious
impact on the performance of wireless multimedia sensor network (WMSN). In this paper, we
propose a pair-wise directional geographical routing (PWDGR) strategy to solve the energy
bottleneck problem. First, the source node can send the data to the pair-wise node around the
sink node in accordance with certain algorithm and then it will send the data to the sink node.
These pair-wise nodes are equally selected in 360° scope around sink according to a certain
algorithm. Therefore, it can effectively relieve the serious energy burden around Sink and also
make a balance between energy consumption and end-to-end delay. Theoretical analysis and a
lot of simulation experiments on PWDGR have been done and the results indicate that PWDGR
is superior to the proposed strategies of the similar strategies both in the view of the theory and
the results of those simulation experiments. With respect to the strategies of the same kind,
PWDGR is able to prolong 70% network life. The delay time is also measured and it is only
increased by 8.1% compared with the similar strategies.

A Spectral Clustering Approach to Identifying Cuts in Wireless Sensor Networks
Wireless sensor networks (WSNs) often suffer from the disrupted connectivity due to
unpredictable wireless channels, early depletion of node energy, and physical tampering by
hostile users. The existence of a disconnected segment of the network referred to as network cut,
leads to data loss, wasted power consumption, and congestion in the WSN. However, existing
approaches to network cut detection in the WSN rely on the assumption that a node or a link
either works normally or fails, without considering the uncertain and random features of wireless
links in the WSN. In this paper, we extend the notion of the network cut based on the realistic
wireless channel model. Furthermore, we formulate the problem of minimizing the normalized
cut (Ncut) with critical nodes, considering the quality of wireless links, degree weights, and
different priorities of sensor nodes. Then, we propose a network cut identification algorithm and
dominant eigenvector computation algorithm that efficiently identify multiple network cuts by
computing multiple eigenvalues and eigenvectors according to a given parameter of eigenvalue
gap. Extensive simulations are conducted to examine the effectiveness and robustness of the
proposed approach. The results show that the proposed method strikes a balance between
minimizing the Ncut objective and the degree of disconnection of critical nodes and achieves a
better performance than existing algorithms.
Non cooperative Game-Based Energy Welfare Topology Control for Wireless Sensor
Networks
In this paper, we address the problem of minimizing energy consumption and balancing energy
in a wireless sensor network, using a topology control algorithm. Such an algorithm is able to
minimize and balance energy consumption by reasonably tuning the transmission power level
while preserving network connectivity. This paper proposes an energy welfare topology control
using game theory approach, which adopts the welfare function from social sciences to compute
energy welfare as a goodness measure for energy populations. When each node tries to maximize
the energy welfare of its local society, it collectively leads to energy balancing. We show that the
resulting game is a potential game and that it possesses a unique Nash equilibrium, which is
Pareto optimal. To evaluate the performance of the proposed algorithm, extensive simulations

were carried out, and the results were compared with the existing algorithm. The results
demonstrated the superiority of the proposed algorithm over the existing algorithm.
Opportunistic Routing Algorithm for Relay Node Selection in Wireless Sensor Networks
Energy savings optimization becomes one of the major concerns in the wireless sensor network
(WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the
limited nonrechargeable battery power. In this paper, we focus on minimizing energy
consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue
network. Following the principle of opportunistic routing theory, multihop relay decision to
optimize the network energy efficiency is made based on the differences among sensor nodes, in
terms of both their distance to sink and the residual energy of each other. Specifically, an Energy
Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power
cost during data relay and protect the nodes with relatively low residual energy. Extensive
simulations and real testbed results show that the proposed solution ENS_OR can significantly
improve the network performance on energy saving and wireless connectivity in comparison
with other existing WSN routing schemes.
An Efficient Cluster-Tree Based Data Collection Scheme for Large Mobile Wireless
Sensor Networks
Amidst of the growing impact of wireless sensor networks (WSNs) on real world applications,
numerous schemes have been proposed for collecting data on multipath routing, tree, clustering,
and cluster tree. Effectiveness of WSNs only depends on the data collection schemes. Existing
methods cannot provide a guaranteed reliable network about mobility, traffic, and end-to-end
connection, respectively. To mitigate such kind of problems, a simple and effective scheme is
proposed, which is named as cluster independent data collection tree (CIDT). After the cluster
head election and cluster formation, CIDT constructs a data collection tree (DCT) based on the
cluster head location. In DCT, data collection node (DCN) does not participate in sensing, which

is simply collecting the data packet from the cluster head and delivering it into sink. CIDT
minimizes the energy exploitation, end-to-end delay and traffic of cluster head due to transfer of
data with DCT. CIDT provides less complexity involved in creating a tree structure, which
maintains the energy consumption of cluster head that helps to reduce the frequent cluster
formation and maintain a cluster for considerable amount of time. The simulation results show
that CIDT provides better QoS in terms of energy consumption, throughput, end-to-end delay,
and network lifetime for mobility-based WSNs.
Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor
Network With Isolated Nodes
A suitable clustering algorithm for grouping sensor nodes can increase the energy efficiency of
WSNs. However, clustering requires additional overhead, such as cluster head selection and
assignment, and cluster construction. This paper proposes a new regional energy aware
clustering method using isolated nodes for WSNs, called Regional Energy Aware Clustering
with Isolated Nodes (REAC-IN). In REAC-IN, CHs are selected based on weight. Weight is
determined according to the residual energy of each sensor and the regional average energy of all
sensors in each cluster. Improperly designed distributed clustering algorithms can cause nodes to
become isolated from CHs. Such isolated nodes communicate with the sink by consuming excess
amount of energy. To prolong network lifetime, the regional average energy and the distance
between sensors and the sink are used to determine whether the isolated node sends its data to a
CH node in the previous round or to the sink. The simulation results of the current study revealed
that REAC-IN outperforms other clustering algorithms.
Lightweight Self-Adapting Linear Prediction Algorithms for Wireless Sensor Networks
In wireless sensor networks, data prediction is an efficient technique to reduce the number of
redundant data transmissions for applications that require sensor nodes to regularly report their
readings. This paper proposes a series of novel self-adapting linear prediction algorithms for the

sensor nodes to report their readings to the sink or to the cluster head when clustering technology
is used. We propose a dynamical extraction algorithm to select a suitable training set from the
history time series data; we propose an information criterion-based searching algorithm to find a
better training set if the chosen training set is not valid for the training of the new predictors; and
we propose an exception detection scheme to determine whether the linear predictors are
efficient for data prediction. Experimental results based on the practical temperature time series
data demonstrate the efficiency of the proposed algorithms, and our prediction algorithms show a
significant improvement of the performance in reducing the number of data transmissions and
the transmission energy cost.
Energy Management and Cross Layer Optimization for Wireless Sensor Network
Powered by Heterogeneous Energy Sources
Recently, utilizing renewable energy for wireless system has attracted extensive attention.
However, due to the instable energy supply and the limited battery capacity, renewable energy
cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The
coexistence of renewable energy and electricity grid is expected as a promising energy supply
manner to remain function for a potentially infinite lifetime. In this paper, we propose a new
system model suitable for WSN, taking into account multiple energy consumptions due to
sensing, transmission and reception, heterogeneous energy supplies from renewable energy,
electricity grid and mixed energy, and multidimension stochastic natures due to energy
harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer
optimization problem is formulated to achieve the optimal trade-off between the time-average
rate utility and electricity cost subject to the data and energy queuing stability constraints. The
Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is
applied to obtain a fully distributed and low-complexity cross-layer algorithm only requiring
knowledge of the instantaneous system state. The explicit trade-off between the optimization
objective and queue backlog is theoretically proven. Finally, the extensive simulations verify the
theoretic claims.

A Secure Scheme Against Power Exhausting Attacks in Hierarchical Wireless Sensor
Networks
Security and energy efficiency are critical concerns in wireless sensor network (WSN) design.
This paper aims to develop an energy-efficient secure scheme against power exhausting attacks,
especially the denial-of-sleep attacks, which can shorten the lifetime of WSNs rapidly. Although
various media access control (MAC) protocols have been proposed to save the power and extend
the lifetime of WSNs, the existing designs of MAC protocol are insufficient to protect the WSNs
from denial-of-sleep attacks in MAC layer. This is attributed to the fact that the well-known
security mechanisms usually awake the sensor nodes before these nodes are allowed to execute
the security processes. Therefore, the practical design is to simplify the authenticating process in
order to reduce the energy consumption of sensor nodes and enhance the performance of the
MAC protocol in countering the power exhausting attacks. This paper proposes a cross-layer
design of secure scheme integrating the MAC protocol. The analyses show that the proposed
scheme can counter the replay attack and forge attack in an energy-efficient way. The detailed
analysis of energy distribution shows a reasonable decision rule of coordination between energy
conservation and security requirements for WSNs.
ACPN: A Novel Authentication Framework with Conditional Privacy-Preservation and
Non-Repudiation for VANETs
In Vehicular Ad hoc NETworks (VANETs), authentication is a crucial security service for both
inter-vehicle and vehicle-roadside communications. On the other hand, vehicles have to be
protected from the misuse of their private data and the attacks on their privacy, as well as to be
capable of being investigated for accidents or liabilities from non-repudiation. In this paper, we
investigate the authentication issues with privacy preservation and non-repudiation in VANETs.
We propose a novel framework with preservation and repudiation (ACPN) for VANETs. In
ACPN, we introduce the public-key cryptography (PKC) to the pseudonym generation, which
ensures legitimate third parties to achieve the non-repudiation of vehicles by obtaining vehicles'
real IDs. The self-generated PKCbased pseudonyms are also used as identifiers instead of vehicle
IDs for the privacy-preserving authentication, while the update of the pseudonyms depends on

vehicular demands. The existing ID-based signature (IBS) scheme and the ID-based
online/offline signature (IBOOS) scheme are used, for the authentication between the road side
units (RSUs) and vehicles, and the authentication among vehicles, respectively. Authentication,
privacy preservation, non-repudiation and other objectives of ACPN have been analyzed for
VANETs. Typical performance evaluation has been conducted using efficient IBS and IBOOS
schemes. We show that the proposed ACPN is feasible and adequate to be used efficiently in the
VANET environment.
Energy-Efficient Scheduling in Green Vehicular Infrastructure With Multiple Roadside
Units
In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green
vehicular roadside infrastructure. In multiple roadside unit (RSU) deployments, the energy
provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a
normalized min-max energy viewpoint. This paper considers both splittable RSU assignment
(SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear
programming bound is first derived for normalized min-max RSU energy usage. We then show
that in the SRA case, there is a polynomial complexity 2-approximation bound for the
normalized min-max energy schedule. This paper then proposes several online scheduling
algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by
minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-
approximation online algorithm (TOAA)], which is an online version of the 2-approximation
bound. Two algorithms are then introduced based on a potential function scheduling approach.
The 1-objective algorithm uses an objective based on normalized min-max energy, and we show
that it has an upper bounded worst-case competitive ratio performance. The 2-objective
algorithm uses the same approach but incorporates a total-energy secondary objective as well.
Results from a variety of experiments show that the proposed scheduling algorithms perform
well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the
lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives.
In the URA case, our low-complexity 1-objective algorithm performs better than the others over
a wide range of traffic conditions.

Delay-Constrained Data Aggregation in VANETs
Data aggregation has been recognized as an effective technique for reducing communication
costs while obtaining useful aggregated information. In this paper, we study the crucial problem
of delay-constrained data aggregation in vehicular ad hoc networks (VANETs), which has not
been well studied in the literature. With the analysis based on real traces, we observe that there is
heterogeneity with node contact patterns, which indicates that some nodes contact other nodes
more frequently. Motivated by this observation, we propose an approach called aTree. The
centralized aTree first constructs a data aggregation tree based on the shortest path tree and then
assigns a waiting time budget to each node on the tree based on dynamic programming. We
further develop a distributed aTree, in which a shortest path tree is built in a distributed fashion,
and nodes determine their waiting time budgets collaboratively. We have performed extensive
simulations on real taxi traces, and results show that our aTree schemes incur much lower
transmission overhead while achieving the same performance compared with other schemes.
An Evolutionary Game Theory-Based Approach to Cooperation in VANETs Under
Different Network Conditions
Vehicular Ad hoc NETworks (VANETs) belong to a class of complex networks due to constant
addition and deletion of nodes. Stimulating cooperation in these networks is a research challenge
due to this uncertainty. The reason is that the node behavior is highly influenced by the
neighborhood structure. Game theory has been significantly used to model ad hoc networks and
optimize cooperation. However, in vehicular interactions, apart from the individual node
behavior, networking properties play a vital role in the evolution of cooperation. This paper
presents a public goods game (PGG) group interaction model for vehicular networks. We
analyze how networking properties can impact the diffusion of cooperation. Simulation results
show that higher network connectivity induces higher clustering in the network. This influences
the probability of nodes receiving common packets from the neighborhood. The average path
length proportional to clustering impacts the benefit sharing in the neighborhood. Results show
that cooperation diffusion in these networks cannot be forced but evolves with different
networking conditions.

Speed Adaptive Probabilistic Flooding for Vehicular Ad Hoc Networks
A significant issue in vehicular ad hoc networks is the design of an effective broadcast scheme
which can facilitate the fast and reliable dissemination of emergency warning messages in the
vicinity of an expected event, such as a car accident. In this work we propose a novel solution to
this problem, which we refer to as Speed Adaptive Probabilistic Flooding. The scheme employs
probabilistic flooding to mitigate the effects of the broadcast storm problem, typical when using
blind flooding, and its unique feature is that the rebroadcast probability is regulated adaptively
based on the vehicle speed to account for varying traffic densities within the transportation
network. The protocol enjoys a number of benefits relative to other approaches: it is simple to
implement, it does not introduce additional communication burden, as it relies on local
information only and it does not rely on the existence of a positioning system which may not
always be available. The scheme is evaluated on different sections of the highway system in the
City of Los Angeles using an integrated platform combining the OPNET Modeler and the
VISSIM simulator. Simulation results indicate that the proposed scheme fulfills its design
objectives as it achieves high reachability and low latency of message delivery in a number of
scenarios. Its robustness with respect to changing number of hops and transmission ranges is also
demonstrated.
A Novel Centralized TDMA-Based Scheduling Protocol for Vehicular Networks
In this paper, we propose a novel centralized time-division multiple access (TDMA)-based
scheduling protocol for practical vehicular networks based on a new weight-factor-based
scheduler. A roadside unit (RSU), as a centralized controller, collects the channel state
information and the individual information of the communication links within its communication
coverage, and it calculates their respective scheduling weight factors, based on which scheduling
decisions are made by the RSU. Our proposed scheduling weight factor mainly consists of three
parts, i.e., the channel quality factor, the speed factor, and the access category factor. In addition,

a resource-reusing mode among multiple vehicle-to-vehicle (V2V) links is permitted if the
distances between every two central vehicles of these V2V links are larger than a predefined
interference interval. Compared with the existing medium-access-control protocols in vehicular
networks, the proposed centralized TDMA-based scheduling protocol can significantly improve
the network throughput and can be easily incorporated into practical vehicular networks.
An Efficient Cluster-Tree Based Data Collection Scheme for Large Mobile Wireless
Sensor Networks
Amidst of the growing impact of wireless sensor networks (WSNs) on real world applications,
numerous schemes have been proposed for collecting data on multipath routing, tree, clustering,
and cluster tree. Effectiveness of WSNs only depends on the data collection schemes. Existing
methods cannot provide a guaranteed reliable network about mobility, traffic, and end-to-end
connection, respectively. To mitigate such kind of problems, a simple and effective scheme is
proposed, which is named as cluster independent data collection tree (CIDT). After the cluster
head election and cluster formation, CIDT constructs a data collection tree (DCT) based on the
cluster head location. In DCT, data collection node (DCN) does not participate in sensing, which
is simply collecting the data packet from the cluster head and delivering it into sink. CIDT
minimizes the energy exploitation, end-to-end delay and traffic of cluster head due to transfer of
data with DCT. CIDT provides less complexity involved in creating a tree structure, which
maintains the energy consumption of cluster head that helps to reduce the frequent cluster
formation and maintain a cluster for considerable amount of time. The simulation results show
that CIDT provides better QoS in terms of energy consumption, throughput, end-to-end delay,
and network lifetime for mobility-based WSNs.

Multi-Node Wireless Energy Charging in Sensor Networks
Wireless energy transfer based on magnetic resonant coupling is a promising technology to
replenish energy to a wireless sensor network (WSN). However, charging sensor nodes one at a
time poses a serious scalability problem. Recent advances in magnetic resonant coupling show
that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node
wireless energy transfer technology and investigate whether it is a scalable technology to address
energy issues in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling
inside a WSN and charging sensor nodes wirelessly. Based on charging range of the WCV, we
propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal
cells. We pursue a formal optimization framework by jointly optimizing traveling path, flow
routing, and charging time. By employing discretization and a novel Reformulation-
Linearization Technique (RLT), we develop a provably near-optimal solution for any desired
level of accuracy. Through numerical results, we demonstrate that our solution can indeed
address the charging scalability problem in a WSN.
Analysis of a “/0” Stealth Scan From a Botnet
Botnets are the most common vehicle of cyber-criminal activity. They are used for spamming,
phishing, denial of service attacks, brute-force cracking, stealing private information, and cyber
warfare. Botnets carry out network scans for several reasons, including searching for vulnerable
machines to infect and recruit into the botnet, probing networks for enumeration or penetration,
etc. We present the measurement and analysis of a horizontal scan of the entire IPv4 address
space conducted by the Sality botnet in February of last year. This 12-day scan originated from
approximately 3 million distinct IP addresses, and used a heavily coordinated and unusually
covert scanning strategy to try to discover and compromise VoIP-related (SIP server)
infrastructure. We observed this event through the UCSD Network Telescope, a /8 darknet
continuously receiving large amounts of unsolicited traffic, and we correlate this traffic data with
other public sources of data to validate our inferences. Sality is one of the largest botnets ever

identified by researchers, its behavior represents ominous advances in the evolution of modern
malware: the use of more sophisticated stealth scanning strategies by millions of coordinated
bots, targeting critical voice communications infrastructure. This work offers a detailed
dissection of the botnet‛s scanning behavior, including general methods to correlate, visualize,
and extrapolate botnet behavior across the global Internet.
Learning-Based Uplink Interference Management in 4G LTE Cellular Systems
LTE's uplink (UL) efficiency critically depends on how the interference across different cells is
controlled. The unique characteristics of LTE's modulation and UL resource assignment poses
considerable challenges in achieving this goal because most LTE deployments have 1:1
frequency reuse, and the uplink interference can vary considerably across successive time-slots.
In this paper, we propose LeAP, a measurement data-driven machine learning paradigm for
power control to manage uplink interference in LTE. The data-driven approach has the inherent
advantage that the solution adapts based on network traffic, propagation, and network topology,
which is increasingly heterogeneous with multiple cell-overlays. LeAP system design consists of
the following components: 1) design of user equipment (UE) measurement statistics that are
succinct, yet expressive enough to capture the network dynamics, and 2) design of two learning-
based algorithms that use the reported measurements to set the power control parameters and
optimize the network performance. LeAP is standards-compliant and can be implemented in a
centralized self-organized networking (SON) server resource (cloud). We perform extensive
evaluations using radio network plans from a real LTE network operational in a major metro area
in the US. Our results show that, compared to existing approaches, LeAP provides 4.9× gain in
the 20th percentile of user data rate, 3.25× gain in median data rate.

DTN-FLOW: Inter-Landmark Data Flow for High-Throughput Routing in DTNs
In this paper, we focus on the efficient routing of data among different areas in Delay Tolerant
Networks (DTNs). In current algorithms, packets are forwarded gradually through nodes with
higher probability of visiting the destination node or area. However, the number of such nodes
usually is limited, leading to insufficient throughput performance. To solve this problem, we
propose an inter-landmark data routing algorithm, namely DTN-FLOW. It selects popular places
that nodes visit frequently as landmarks and divides the entire DTN area into sub-areas
represented by landmarks. Nodes transiting between landmarks relay packets among landmarks,
even though they rarely visit the destinations of these packets. Specifically, the number of node
transits between two landmarks is measured to represent the forwarding capacity between them,
based on which routing tables are built on each landmark to guide packet routing. Each node
predicts its transits based on its previous landmark visiting records using the order-k Markov
predictor. In a packet routing, a landmark determines the next hop landmark based on its routing
table, and forwards the packet to the node with the highest probability of transiting to the
selected landmark. Thus, DTN-FLOW fully utilizes all node movements to route packets along
landmark paths to their destinations. We analyzed two real DTN traces to support the design of
DTN-FLOW. We also deployed a small DTN-FLOW system in our campus for performance
evaluation. This deployment and trace-driven simulation demonstrate the high efficiency of
DTN-FLOW in comparison with state-of-the-art DTN routing algorithms.
DTN-Meteo: Forecasting the Performance of DTN Protocols Under Heterogeneous
Mobility
Opportunistic or delay-tolerant networks (DTNs) may be used to enable communication in case
of failure or lack of infrastructure (disaster, censorship, remote areas) and to complement
existing wireless technologies (cellular, WiFi). Wireless peers communicate when in contact,
forming an impromptu network, whose connectivity graph is highly dynamic and only partly
connected. In this harsh environment, communication algorithms are mostly local search

heuristics, choosing a solution among the locally available ones. Furthermore, they are routinely
evaluated through simulations only, as they are hard to model analytically. Even when more
insight is sought from models, these usually assume homogeneous node meeting rates, thereby
ignoring the attested heterogeneity and nontrivial structure of human mobility. We propose
DTN-Meteo, a new unified analytical model that maps an important class of DTN optimization
problems over heterogeneous mobility/contact models into a Markov chain traversal over the
relevant solution space. (Heterogeneous) meeting probabilities between different pairs of nodes
dictate the chain's transition probabilities and determine neighboring solutions. Local
optimization algorithms can accept/reject candidate transitions (deterministically or randomly),
thus “modulating” the above transition probabilities. We apply our model to two example
problems: routing and content placement. We predict the performance of state-of-the-art
algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that
surprising precision can be achieved against simulations, despite the complexity of the problems
and diversity of settings. To our best knowledge, this is the first analytical work that can
accurately predict performance for utility-based algorithms and heterogeneous node contact
rates.
Analysis of Application-Layer Filtering Policies With Application to HTTP
Application firewalls are increasingly used to inspect upper-layer protocols (as HTTP) that are
the target or vehicle of several attacks and are not properly addressed by network firewalls. Like
other security controls, application firewalls need to be carefully configured, as errors have a
significant impact on service security and availability. However, currently no technique is
available to analyze their configuration for correctness and consistency. This paper extends a
previous model for analysis of packet filters to the policy anomaly analysis in application
firewalls. Both rule-pair and multirule anomalies are detected, hence reducing the likelihood of
conflicting and suboptimal configurations. The expressiveness of this model has been
successfully tested against the features of Squid, a popular Web caching proxy offering various
access control capabilities. The tool implementing this model has been tested on various
scenarios and exhibits good performance.

A Graph-Theoretic Approach to Scheduling in Cognitive Radio Networks
We focus on throughput-maximizing, max-min fair, and proportionally fair scheduling problems
for centralized cognitive radio networks. First, we propose a polynomial-time algorithm for the
throughput-maximizing scheduling problem. We then elaborate on certain special cases of this
problem and explore their combinatorial properties. Second, we prove that the max-min fair
scheduling problem is NP-Hard in the strong sense. We also prove that the problem cannot be
approximated within any constant factor better than 2 unless P=NP. Additionally, we propose an
approximation algorithm for the max-min fair scheduling problem with approximation ratio
depending on the ratio of the maximum possible data rate to the minimum possible data rate of a
secondary users. We then focus on the combinatorial properties of certain special cases and
investigate their relation with various problems such as the multiple-knapsack, matching,
terminal assignment, and Santa Claus problems. We then prove that the proportionally fair
scheduling problem is NP-Hard in the strong sense and inapproximable within any additive
constant less than log(4/3). Finally, we evaluate the performance of our approximation algorithm
for the max-min fair scheduling problem via simulations. This approach sheds light on the
complexity and combinatorial properties of these scheduling problems, which have high practical
importance in centralized cognitive radio networks.
A Traffic Load Balancing Framework for Software-Defined Radio Access Networks
Powered by Hybrid Energy Sources
Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations
(SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile
networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall
power consumption of a large number of SCBSs is phenomenal. As the energy harvesting
technology advances, base stations (BSs) can be powered by green energy to alleviate the on-

grid power consumption. For mobile networks with high BS density, traffic load balancing is
critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable
to incorporate the green energy utilization as a performance metric in traffic load balancing
strategies. In this paper, we have proposed a traffic load balancing framework that strives a
balance between network utilities, e.g., the average traffic delivery latency, and the green energy
utilization. Various properties of the proposed framework have been derived. Leveraging the
software-defined radio access network architecture, the proposed scheme is implemented as a
virtually distributed algorithm, which significantly reduces the communication overheads
between users and BSs. The simulation results show that the proposed traffic load balancing
framework enables an adjustable trade-off between the on-grid power consumption and the
average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30%, at
a cost of only a small increase, e.g., 8%, of the average traffic delivery latency.
Wireless Network Intrinsic Secrecy
Wireless secrecy is essential for communication confidentiality, health privacy, public safety,
information superiority, and economic advantage in the modern information society.
Contemporary security systems are based on cryptographic primitives and can be complemented
by techniques that exploit the intrinsic properties of a wireless environment. This paper develops
a foundation for design and analysis of wireless networks with secrecy provided by intrinsic
properties such as node spatial distribution, wireless propagation medium, and aggregate
network interference. We further propose strategies that mitigate eavesdropping capabilities, and
we quantify their benefits in terms of network secrecy metrics. This research provides insights
into the essence of wireless network intrinsic secrecy and offers a new perspective on the role of
network interference in communication confidentiality.

FMTCP: A Fountain Code-Based Multipath Transmission Control Protocol
Ideally, the throughput of a Multipath TCP (MPTCP) connection should be as high as that of
multiple disjoint single-path TCP flows. In reality, the throughput of MPTCP is far lower than
expected. In this paper, we conduct an extensive simulation-based study on this phenomenon,
and the results indicate that a subflow experiencing high delay and loss severely affects the
performance of other subflows, thus becoming the bottleneck of the MPTCP connection and
significantly degrading the aggregate goodput. To tackle this problem, we propose Fountain
code-based Multipath TCP (FMTCP), which effectively mitigates the negative impact of the
heterogeneity of different paths. FMTCP takes advantage of the random nature of the fountain
code to flexibly transmit encoded symbols from the same or different data blocks over different
subflows. Moreover, we design a data allocation algorithm based on the expected packet arriving
time and decoding demand to coordinate the transmissions of different subflows. Quantitative
analyses are provided to show the benefit of FMTCP. We also evaluate the performance of
FMTCP through ns-2 simulations and demonstrate that FMTCP outperforms IETF-MPTCP, a
typical MPTCP approach, when the paths have diverse loss and delay in terms of higher total
goodput, lower delay, and jitter. In addition, FMTCP achieves high stability under abrupt
changes of path quality.
Backpressure Delay Enhancement for Encounter-Based Mobile Networks While
Sustaining Throughput Optimality
Backpressure routing, in which packets are preferentially transmitted over links with high queue
differentials, offers the promise of throughput-optimal operation for a wide range of
communication networks. However, when traffic load is low, backpressure methods suffer from
long delays. This is of particular concern in intermittent encounter-based mobile networks which
are already delay-limited due to the sparse and highly dynamic network connectivity. While state
of the art mechanisms for such networks have proposed the use of redundant transmissions to
improve delay, they do not work well when traffic load is high. In this paper we propose

backpressure with adaptive redundancy (BWAR), a novel hybrid approach that provides the best
of both worlds. This approach is robust, distributed, and does not require any prior knowledge of
network load conditions. We also present variants of BWAR that remove redundant packets via a
timeout mechanism, and that improve energy use. These algorithms are evaluated by
mathematical analysis and by simulations of real traces of taxis in Beijing, China. The
simulations confirm that BWAR outperforms traditional backpressure at low load, while
outperforming encounter-routing schemes (Spray and Wait and Spray and Focus) at high load.
A Poisson Hidden Markov Model for Multiview Video Traffic
Multiview video has recently emerged as a means to improve user experience in novel
multimedia services. We propose a new stochastic model to characterize the traffic generated by
a Multiview Video Coding (MVC) variable bit-rate source. To this aim, we resort to a Poisson
hidden Markov model (P-HMM), in which the first (hidden) layer represents the evolution of the
video activity and the second layer represents the frame sizes of the multiple encoded views. We
propose a method for estimating the model parameters in long MVC sequences. We then present
extensive numerical simulations assessing the model's ability to produce traffic with realistic
characteristics for a general class of MVC sequences. We then extend our framework to network
applications where we show that our model is able to accurately describe the sender and receiver
buffers behavior in MVC transmission. Finally, we derive a model of user behavior for
interactive view selection, which, in conjunction with our traffic model, is able to accurately
predict actual network load in interactive multiview services.

Backoff Design for IEEE 802.11 DCF Networks: Fundamental Tradeoff and Design
Criterion
Binary Exponential Backoff (BEB) is a key component of the IEEE 802.11 DCF protocol. It has
been shown that BEB can achieve the theoretical limit of throughput as long as the initial backoff
window size is properly selected. It, however, suffers from significant delay degradation when
the network becomes saturated. It is thus of special interest for us to further design backoff
schemes for IEEE 802.11 DCF networks that can achieve comparable throughput as BEB, but
provide better delay performance. This paper presents a systematic study on the effect of backoff
schemes on throughput and delay performance of saturated IEEE 802.11 DCF networks. In
particular, a backoff scheme is defined as a sequence of backoff window sizes {Wi}. The
analysis shows that a saturated IEEE 802.11 DCF network has a single steady-state operating
point as long as {Wi} is a monotonic increasing sequence. The maximum throughput is found to
be independent of {Wi}, yet the growth rate of {Wi} determines a fundamental tradeoff between
throughput and delay performance. For illustration, Polynomial Backoff is proposed, and the
effect of polynomial power x on the network performance is characterized. It is demonstrated
that Polynomial Backoff with a larger x is more robust against the fluctuation of the network
size, but in the meanwhile suffers from a larger second moment of access delay. Quadratic
Backoff (QB), i.e., Polynomial Backoff with x=2, stands out to be a favorable option as it strikes
a good balance between throughput and delay performance. The comparative study between QB
and BEB confirms that QB well preserves the robust nature of BEB and achieves much better
queueing performance than BEB.
Connectivity-Based Segmentation in Large-Scale 2-D/3-D Sensor Networks: Algorithm
and Applications
Efficient sensor network design requires a full understanding of the geometric environment in
which sensor nodes are deployed. In practice, a large-scale sensor network often has a complex
and irregular topology, possibly containing obstacles/holes. Convex network partitioning, also

known as convex segmentation, is a technique to divide a network into convex regions in which
traditional algorithms designed for a simple network geometry can be applied. Existing
segmentation algorithms heavily depend on concave node detection, or sink extraction from the
median axis/skeleton, resulting in sensitivity of performance to network boundary noise.
Furthermore, since they rely on the network's 2-D geometric properties, they do not work for 3-D
cases. This paper presents a novel segmentation approach based on Morse function, bringing
together the notions of convex components and the Reeb graph of a network. The segmentation
is realized by a distributed and scalable algorithm, named CONSEL, for CONnectivity-based
SEgmentation in Large-scale 2-D/3-D sensor networks. In CONSEL, several boundary nodes
first flood the network to construct the Reeb graph. The ordinary nodes then compute mutex
pairs locally, generating a coarse segmentation. Next, neighboring regions that are not mutex
pairs are merged together. Finally, by ignoring mutex pairs that lead to small concavity, we
provide an approximate convex decomposition. CONSEL has a number of advantages over
previous solutions: 1) it works for both 2-D and 3-D sensor networks; 2) it uses merely network
connectivity information; 3) it guarantees a bound for the generated regions' deviation from
convexity. We further propose to integrate network segmentation with existing applications that
are oriented to simple network geometry. Extensive simulations show the efficacy of CONSEL
in segmenting networks and in improving the performance of two applications: geographic
routing and connectivity-based localization.
On Asymptotic Statistics for Geometric Routing Schemes in Wireless Ad Hoc Networks
In this paper we present a methodology employing statistical analysis and stochastic geometry to
study geometric routing schemes in wireless ad-hoc networks. In particular, we analyze the
network layer performance of one such scheme, the random frac{1}{2}disk routing scheme,
which is a localized geometric routing scheme in which each node chooses the next relay
randomly among the nodes within its transmission range and in the general direction of the
destination. The techniques developed in this paper enable us to establish the asymptotic
connectivity and the convergence results for the mean and variance of the routing path lengths
generated by geometric routing schemes in random wireless networks. In particular, we
approximate the progress of the routing path towards the destination by a Markov process and

determine the sufficient conditions that ensure the asymptotic connectivity for both dense and
large-scale ad-hoc networks deploying the random frac{1}{2}disk routing scheme.
Furthermore, using this Markov characterization, we show that the expected length (hop-count)
of the path generated by the random frac{1}{2}disk routing scheme normalized by the length of
the path generated by the ideal direct-line routing, converges to 3pi/4asymptotically. Moreover,
we show that the variance-to-mean ratio of the routing path length converges to 9pi^2/64-
1 asymptotically. Through simulation, we show that the aforementioned asymptotic statistics are
in fact quite accurate even for finite granularity and size of the network.
Efficient Allocation of Periodic Feedback Channels in Broadband Wireless Networks
Advanced wireless technologies such as multiple-input–multiple-output (MIMO) require each
mobile station (MS) to send a lot of feedback to the base station. This periodic feedback
consumes much of the uplink bandwidth. This expensive bandwidth is very often viewed as a
major obstacle to the deployment of MIMO and other advanced closed-loop wireless
technologies. This paper is the first to propose a framework for efficient allocation of periodic
feedback channels to the nodes of a wireless network. Several relevant optimization problems are
defined and efficient algorithms for solving them are presented. A scheme for deciding when the
base station (BS) should invoke each algorithm is also proposed and shown through simulations
to perform very well.
Fast and Accurate Estimation of RFID Tags
Radio frequency identification (RFID) systems have been widely deployed for various
applications such as object tracking, 3-D positioning, supply chain management, inventory
control, and access control. This paper concerns the fundamental problem of estimating RFID
tag population size, which is needed in many applications such as tag identification, warehouse
monitoring, and privacy-sensitive RFID systems. In this paper, we propose a new scheme for
estimating tag population size called Average Run-based Tag estimation (ART). The technique
is based on the average run length of ones in the bit string received using the standardized
framed slotted Aloha protocol. ART is significantly faster than prior schemes. For example,

given a required confidence interval of 0.1% and a required reliability of 99.9%, ART is
consistently 7 times faster than the fastest existing schemes (UPE and EZB) for any tag
population size. Furthermore, ART's estimation time is provably independent of the tag
population sizes. ART works with multiple readers with overlapping regions and can estimate
sizes of arbitrarily large tag populations. ART is easy to deploy because it neither requires
modification to tags nor to the communication protocol between tags and readers. ART only
needs to be implemented on readers as a software module.
Achieving Optimal Throughput Utility and Low Delay With CSMA-Like Algorithms: A
Virtual Multichannel Approach
Carrier-sense multiple access (CSMA) algorithms have recently received significant interests in
the literature for designing wireless control algorithms. CSMA algorithms incur low complexity
and can achieve the optimal capacity under certain assumptions. However, CSMA algorithms
suffer the starvation problem and incur large delay that may grow exponentially with the network
size. In this paper, our goal is to develop a new algorithm that can provably achieve high
throughput utility and low delay with low complexity. Toward this end, we propose a new
CSMA-like algorithm, called Virtual-Multi-Channel CSMA (VMC-CSMA), that can
dramatically reduce delay. The key idea of VMC-CSMA to avoid the starvation problem is to
use multiple virtual channels (which emulate a multichannel system) and compute a good set of
feasible schedules simultaneously (without constantly switching/recomputing schedules). Under
the protocol interference model and a single-hop utility-maximization setting, VMC-CSMA can
approach arbitrarily close-to-optimal system utility with both the number of virtual channels and
the computation complexity increasing logarithmically with the network size. Furthermore, once
VMC-CSMA converges to the steady state, we can show that under certain assumptions on the
utility functions and the topology, both the expected packet delay and the tail distribution of the
head-of-line (HOL) waiting time at each link can be bounded independently of the network size.
Our simulation results confirm that VMC-CSMA algorithms indeed achieve both high
throughput utility and low delay with low-complexity operations.

Receiver-Based Flow Control for Networks in Overload
We consider utility maximization in networks where the sources do not employ flow control and
may consequently overload the network. In the absence of flow control at the sources, some
packets will inevitably have to be dropped when the network is in overload. To that end, we first
develop a distributed, threshold-based packet-dropping policy that maximizes the weighted sum
throughput. Next, we consider utility maximization and develop a receiver-based flow control
scheme that, when combined with threshold-based packet dropping, achieves the optimal utility.
The flow control scheme creates virtual queues at the receivers as a push-back mechanism to
optimize the amount of data delivered to the destinations via back-pressure routing. A new
feature of our scheme is that a utility function can be assigned to a collection of flows,
generalizing the traditional approach of optimizing per-flow utilities. Our control policies use
finite-buffer queues and are independent of arrival statistics. Their near-optimal performance is
proved and further supported by simulation results.
Offering Supplementary Network Technologies: Adoption Behavior and Offloading
Benefits
To alleviate the congestion caused by rapid growth in demand for mobile data, wireless service
providers (WSPs) have begun encouraging users to offload some of their traffic onto
supplementary network technologies, e.g., offloading from 3G or 4G to WiFi or femtocells. With
the growing popularity of such offerings, a deeper understanding of the underlying economic
principles and their impact on technology adoption is necessary. To this end, we develop a model
for user adoption of a base technology (e.g., 3G) and a bundle of the base plus a supplementary
technology (e.g., 3G + WiFi). Users individually make their adoption decisions based on several
factors, including the technologies' intrinsic qualities, negative congestion externalities from
other subscribers, and the flat access rates that a WSP charges. We then show how these user-
level decisions translate into aggregate adoption dynamics and prove that these converge to a
unique equilibrium for a given set of exogenously determined system parameters. We fully
characterize these equilibria and study adoption behaviors of interest to a WSP. We then derive

analytical expressions for the revenue-maximizing prices and optimal coverage factor for the
supplementary technology and examine some resulting nonintuitive user adoption behaviors.
Finally, we develop a mobile app to collect empirical 3G/WiFi usage data and numerically
investigate the profit-maximizing adoption levels when a WSP accounts for its cost of deploying
the supplemental technology and savings from offloading traffic onto this technology.
On the Delay Performance in a Large-Scale Wireless Sensor Network: Measurement,
Analysis, and Implications
We present a comprehensive delay performance measurement and analysis in a large-scale
wireless sensor network. We build a lightweight delay measurement system and present a robust
method to calculate the per-packet delay. We show that the method can identify incorrect delays
and recover them with a bounded error. Through analysis of delay and other system metrics, we
seek to answer the following fundamental questions: What are the spatial and temporal
characteristics of delay performance in a real network? What are the most important impacting
factors, and is there any practical model to capture those factors? What are the implications to
protocol designs? In this paper, we identify important factors from the data trace and show that
the important factors are not necessarily the same with those in the Internet. Furthermore, we
propose a delay model to capture those factors. We revisit several prevalent protocol designs
such as Collection Tree Protocol, opportunistic routing, and Dynamic Switching-based
Forwarding and show that our model and analysis are useful to practical protocol designs.
Scheduling in Networks With Time-Varying Channels and Reconfiguration Delay
We consider the optimal control problem for networks subjected to time-varying channels,
reconfiguration delays, and interference constraints. We show that the simultaneous presence of
time-varying channels and reconfiguration delays significantly reduces the system stability
region and changes the structure of optimal policies. We first consider memoryless channel

processes and characterize the stability region in closed form. We prove that a frame-based Max-
Weight scheduling algorithm that sets frame durations dynamically, as a function of the current
queue lengths and average channel gains, is throughput-optimal. Next, we consider arbitrary
Markov-modulated channel processes and show that memory in the channel processes can be
exploited to improve the stability region. We develop a novel approach to characterizing the
stability region of such systems using state-action frequencies, which are stationary solutions to a
Markov Decision Process (MDP) formulation. Moreover, we develop a dynamic control policy
using the state-action frequencies and variable frames whose lengths are functions of queue sizes
and show that it is throughput-optimal. The frame-based dynamic control (FBDC) policy is
applicable to a broad class of network control systems, with or without reconfiguration delays,
and provides a new framework for developing throughput-optimal network control policies using
state-action frequencies. Finally, we propose Myopic policies that are easy to implement and
have better delay properties as compared to the FBDC policy.
Capacity Achieving Distributed Scheduling With Finite Buffers
In this paper, we propose a distributed cross-layer scheduling algorithm for wireless networks
with single-hop transmissions that can guarantee finite buffer sizes and meet minimum utility
requirements. The algorithm can achieve a utility arbitrarily close to the optimal value with a
tradeoff in the buffer sizes. The finite buffer property is not only important from an
implementation perspective, but, along with the algorithm, also yields superior delay
performance. In addition, another extended algorithm is provided to help construct the upper
bounds of per-flow average packet delays. A novel structure of Lyapunov function is employed
to prove the utility optimality of the algorithm with the introduction of novel virtual queue
structures. Unlike traditional back-pressure-based optimal algorithms, our proposed algorithm
does not need centralized computation and achieves fully local implementation without global
message passing. Compared to other recent throughput/utility-optimal CSMA distributed
algorithms, we illustrate through rigorous numerical and implementation results that our
proposed algorithm achieves far better delay performance for comparable throughput/utility
levels.

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