226 research outputs found
Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis
Networked system often relies on distributed algorithms to achieve a global
computation goal with iterative local information exchanges between neighbor
nodes. To preserve data privacy, a node may add a random noise to its original
data for information exchange at each iteration. Nevertheless, a neighbor node
can estimate other's original data based on the information it received. The
estimation accuracy and data privacy can be measured in terms of -data-privacy, defined as the probability of -accurate
estimation (the difference of an estimation and the original data is within
) is no larger than (the disclosure probability). How to
optimize the estimation and analyze data privacy is a critical and open issue.
In this paper, a theoretical framework is developed to investigate how to
optimize the estimation of neighbor's original data using the local information
received, named optimal distributed estimation. Then, we study the disclosure
probability under the optimal estimation for data privacy analysis. We further
apply the developed framework to analyze the data privacy of the
privacy-preserving average consensus algorithm and identify the optimal noises
for the algorithm.Comment: 32 pages, 2 figure
UWB Signal Detection by Cyclic Features
Ultra-wideband (UWB) impulse radio (IR) systems are well known for low
transmission power, low probability of detection, and overlaying with
narrowband (NB) systems. These merits in fact make UWB signal detection
challenging, since several high-power wireless communication systems coexist
with UWB signals. In the literature, cyclic features are exploited for signal
detection. However, the high computational complexity of conventional cyclic
feature based detectors burdens the receivers. In this paper, we propose
computationally efficient detectors using the specific cyclic features of UWB
signals. The closed-form relationships between the cyclic features and the
system parameters are revealed. Then, some constant false alarm rate detectors
are proposed based on the estimated cyclic autocorrelation functions (CAFs).
The proposed detectors have low complexities compared to the existing ones.
Extensive simulation results indicate that the proposed detectors achieve a
good balance between the detection performance and the computational complexity
in various scenarios, such as multipath environments, colored noise, and NB
interferences
YouSense: Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing
Collaborative spectrum sensing has been recognized as a promising approach to
improve the sensing performance via exploiting the spatial diversity of the
secondary users. In this study, a new selfishness issue is identified, that
selfish users sense no spectrum in collaborative sensing. For easier
presentation, it's denoted as entropy selfishness. This selfish behavior is
difficult to distinguish, making existing detection based incentive schemes
fail to work. To thwart entropy selfishness in distributed collaborative
sensing, we propose YouSense, a One-Time Pad (OTP) based incentive design that
could naturally isolate entropy selfish users from the honest users without
selfish node detection. The basic idea of YouSense is to construct a trapdoor
one-time pad for each sensing report by combining the original report and a
random key. Such a one-time pad based encryption could prevent entropy selfish
users from accessing the original sensing report while enabling the honest
users to recover the report. Different from traditional cryptography based OTP
which requires the key delivery, YouSense allows an honest user to recover the
pad (or key) by exploiting a unique characteristic of collaborative sensing
that different secondary users share some common observations on the same radio
spectrum. We further extend YouSense to improve the recovery successful rate by
reducing the cardinality of set of the possible pads. By extensive USRP based
experiments, we show that YouSense can successfully thwart entropy selfishness
with low system overhead.Comment: at INFOCOM'1
Dynamic Sleep Control in Green Relay-Assisted Networks for Energy Saving and QoS Improving
We study the relay station (RS) sleep control mechanism targeting on reducing
energy consumption while improving users' quality of service (QoS) in green
relay-assisted cellular networks, where the base station (BS) is powered by
grid power and the RSs are powered by renewable energy. By adopting green RSs,
the grid power consumption of the BS is greatly reduced. But due to the
uncertainty and stochastic characteristics of the renewable energy, power
supply for RSs is not always sufficient. Thus the harvested energy needs to be
scheduled appropriately to cater to the dynamic traffic so as to minimize the
energy saving in the long term. An optimization problem is formulated to find
the optimal sleep ratio of RSs to match the time variation of energy harvesting
and traffic arrival. To fully use the renewable energy, green-RS-first
principle is adopted in the user association process. The optimal RS sleeping
policy is obtained through dynamic programming (DP) approach, which divides the
original optimization problem into per-stage subproblems. A reduced DP
algorithm and a greedy algorithm are further proposed to greatly reduce the
computation complexity. By simulations, the reduced DP algorithm outperforms
the greedy algorithm in achieving satisfactory energy saving and QoS
performance.Comment: 7 papers, 4 figure
Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships
Optimal power management of shipboard power system for failure mode (OPMSF)
is a significant and challenging problem considering the safety of system and
person. Many existing works focused on the transient-time recovery without
consideration of the operating cost and the voyage plan. In this paper, the
OPMSF problem is formulated considering the mid-time scheduling and the faults
at bus and generator. Two- side adjustment methods including the load shedding
and the reconfiguration are coordinated for reducing the fault effects. To
address the formulated non-convex problem, the travel equality constraint and
fractional energy efficiency operation indicator (EEOI) limitation are
transformed into the convex forms. Then, considering the infeasibility scenario
affected by faults, a further relaxation is adopted to formulate a new problem
with feasibility guaranteed. Furthermore, a sufficient condition is derived to
ensure that the new problem has the same optimal solution as the original one.
Because of the mixed-integer nonlinear feature, an optimal algorithm based on
Benders decomposition (BD) is developed to solve the new one. Due to the slow
convergence caused by the time-coupled constraints, a low-complexity
near-optimal algorithm based on BD (LNBD) is proposed. The results verify the
effectivity of the proposed methods and algorithms.Comment: 14 pages, 9 figures, accepted for publication in IEEE Transactions on
Power System
Cross-Layer Scheduling for OFDMA-based Cognitive Radio Systems with Delay and Security Constraints
This paper considers the resource allocation problem in an Orthogonal
Frequency Division Multiple Access (OFDMA) based cognitive radio (CR) network,
where the CR base station adopts full overlay scheme to transmit both private
and open information to multiple users with average delay and power
constraints. A stochastic optimization problem is formulated to develop flow
control and radio resource allocation in order to maximize the long-term system
throughput of open and private information in CR system and ensure the
stability of primary system. The corresponding optimal condition for employing
full overlay is derived in the context of concurrent transmission of open and
private information. An online resource allocation scheme is designed to adapt
the transmission of open and private information based on monitoring the status
of primary system as well as the channel and queue states in the CR network.
The scheme is proven to be asymptotically optimal in solving the stochastic
optimization problem without knowing any statistical information. Simulations
are provided to verify the analytical results and efficiency of the scheme
Energy Efficient Resource Allocation for Time-Varying OFDMA Relay Systems with Hybrid Energy Supplies
This paper investigates the energy efficient resource allocation for
orthogonal frequency division multiple access (OFDMA) relay systems, where the
system is supplied by the conventional utility grid and a renewable energy
generator equipped with a storage device. The optimal usage of radio resource
depends on the characteristics of the renewable energy generation and the
mobile traffic, which exhibit both temporal and spatial diversities. Lyapunov
optimization method is used to decompose the problem into the joint flow
control, radio resource allocation and energy management without knowing a
priori knowledge of system statistics. It is proven that the proposed algorithm
can result in close-to-optimal performance with capacity limited data buffer
and storage device. Simulation results show that the flexible tradeoff between
the system utility and the conventional energy consumption can be achieved.
Compared with other schemes, the proposed algorithm demonstrates better
performance.Comment: 12 pages, 9 figures, IEEE System Journa
Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking
With onboard operating systems becoming increasingly common in vehicles, the
real-time broadband infotainment and Intelligent Transportation System (ITS)
service applications in fast-motion vehicles become ever demanding, which are
highly expected to significantly improve the efficiency and safety of our daily
on-road lives. The emerging ITS and vehicular applications, e.g., trip
planning, however, require substantial efforts on the real-time pervasive
information collection and big data processing so as to provide quick decision
making and feedbacks to the fast moving vehicles, which thus impose the
significant challenges on the development of an efficient vehicular
communication platform. In this article, we present TrasoNET, an integrated
network framework to provide realtime intelligent transportation services to
connected vehicles by exploring the data analytics and networking techniques.
TrasoNET is built upon two key components. The first one guides vehicles to the
appropriate access networks by exploring the information of realtime traffic
status, specific user preferences, service applications and network conditions.
The second component mainly involves a distributed automatic access engine,
which enables individual vehicles to make distributed access decisions based on
access recommender, local observation and historic information. We showcase the
application of TrasoNET in a case study on real-time traffic sensing based on
real traces of taxis
Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation
Severe pollution induced by traditional fossil fuels arouses great attention
on the usage of plug-in electric vehicles (PEVs) and renewable energy. However,
large-scale penetration of PEVs combined with other kinds of appliances tends
to cause excessive or even disastrous burden on the power grid, especially
during peak hours. This paper focuses on the scheduling of PEVs charging
process among different charging stations and each station can be supplied by
both renewable energy generators and a distribution network. The distribution
network also powers some uncontrollable loads. In order to minimize the on-grid
energy cost with local renewable energy and non-ideal storage while avoiding
the overload risk of the distribution network, an online algorithm consisting
of scheduling the charging of PEVs and energy management of charging stations
is developed based on Lyapunov optimization and Lagrange dual decomposition
techniques. The algorithm can satisfy the random charging requests from PEVs
with provable performance. Simulation results with real data demonstrate that
the proposed algorithm can decrease the time-average cost of stations while
avoiding overload in the distribution network in the presence of random
uncontrollable loads.Comment: 30 pages, 13 figure
Energy Trading in Microgrids for Synergies among Electricity, Hydrogen and Heat Networks
The emerging paradigm of interconnected microgrids advocates energy trading
or sharing among multiple microgrids. It helps make full use of the temporal
availability of energy and diversity in operational costs when meeting various
energy loads. However, energy trading might not completely absorb excess
renewable energy. A multi-energy management framework including fuel cell
vehicles, energy storage, combined heat and power system, and renewable energy
is proposed, and the characteristics and scheduling arrangements of fuel cell
vehicles are considered to further improve the local absorption of the
renewable energy and enhance the economic benefits of microgrids. While
intensive research has been conducted on energy scheduling and trading problem,
a fundamental question still remains unanswered on microgrid economics. Namely,
due to multi-energy coupling, stochastic renewable energy generation and
demands, when and how a microgrid should schedule and trade energy with others,
which maximizes its long-term benefit. This paper designs a joint energy
scheduling and trading algorithm based on Lyapunov optimization and a
double-auction mechanism. Its purpose is to determine the valuations of energy
in the auction, optimally schedule energy distribution, and strategically
purchase and sell energy with the current electricity prices. Simulations based
on real data show that each individual microgrid, under the management of the
proposed algorithm, can achieve a time-averaged profit that is arbitrarily
close to an optimum value, while avoiding compromising its own comfort
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