37,964 research outputs found
Detection of malicious data in vehicular ad-hoc networks for traffic signal control applications
Effective applications of vehicular ad hoc networks in traffic signal control
require new methods for detection of malicious data. Injection of malicious
data can result in significantly decreased performance of such applications,
increased vehicle delays, fuel consumption, congestion, or even safety threats.
This paper introduces a method, which combines a model of expected driver
behaviour with position verification in order to detect the malicious data
injected by vehicle nodes that perform Sybil attacks. Effectiveness of this
approach was demonstrated in simulation experiments for a decentralized
self-organizing system that controls the traffic signals at multiple
intersections in an urban road network. Experimental results show that the
proposed method is useful for mitigating the negative impact of malicious data
on the performance of traffic signal control.Comment: 11 pages, 4 figure
fiction: An Open Source Framework for the Design of Field-coupled Nanocomputing Circuits
As a class of emerging post-CMOS technologies, Field-coupled Nanocomputing
(FCN) devices promise computation with tremendously low energy dissipation.
Even though ground breaking advances in several physical implementations like
Quantum-dot Cellular Automata (QCA) or Nanomagnet Logic (NML) have been made in
the last couple of years, design automation for FCN is still in its infancy and
often still relies on manual labor. In this paper, we present an open source
framework called fiction for physical design and technology mapping of FCN
circuits. Its efficient data structures, state-of-the-art algorithms, and
extensibility provide a basis for future research in the community
On Engineering and Emergence
The engineering and design of self-organizing systems with emergent
properties is a long-standing problem in the field of complex and distributed
systems, for example in the engineering of self-organizing Multi-Agent Systems.
The problem of combining engineering with emergence - to find a simple rule for
a complex pattern - equals the problem of science in general. Therefore the
answers are similar, and the scientific method is the general solution to the
problem of engineering complex systems.Comment: 11 pages, 10 figure
Towards Probabilistic Formal Modeling of Robotic Cell Injection Systems
Cell injection is a technique in the domain of biological cell
micro-manipulation for the delivery of small volumes of samples into the
suspended or adherent cells. It has been widely applied in various areas, such
as gene injection, in-vitro fertilization (IVF), intracytoplasmic sperm
injection (ISCI) and drug development. However, the existing manual and
semi-automated cell injection systems require lengthy training and suffer from
high probability of contamination and low success rate. In the recently
introduced fully automated cell injection systems, the injection force plays a
vital role in the success of the process since even a tiny excessive force can
destroy the membrane or tissue of the biological cell. Traditionally, the force
control algorithms are analyzed using simulation, which is inherently
non-exhaustive and incomplete in terms of detecting system failures. Moreover,
the uncertainties in the system are generally ignored in the analysis. To
overcome these limitations, we present a formal analysis methodology based on
probabilistic model checking to analyze a robotic cell injection system
utilizing the impedance force control algorithm. The proposed methodology,
developed using the PRISM model checker, allowed to find a discrepancy in the
algorithm, which was not found by any of the previous analysis using the
traditional methods.Comment: In Proceedings MARS 2017, arXiv:1703.0581
From 4G to 5G: Self-organized Network Management meets Machine Learning
In this paper, we provide an analysis of self-organized network management,
with an end-to-end perspective of the network. Self-organization as applied to
cellular networks is usually referred to Self-organizing Networks (SONs), and
it is a key driver for improving Operations, Administration, and Maintenance
(OAM) activities. SON aims at reducing the cost of installation and management
of 4G and future 5G networks, by simplifying operational tasks through the
capability to configure, optimize and heal itself. To satisfy 5G network
management requirements, this autonomous management vision has to be extended
to the end to end network. In literature and also in some instances of products
available in the market, Machine Learning (ML) has been identified as the key
tool to implement autonomous adaptability and take advantage of experience when
making decisions. In this paper, we survey how network management can
significantly benefit from ML solutions. We review and provide the basic
concepts and taxonomy for SON, network management and ML. We analyse the
available state of the art in the literature, standardization, and in the
market. We pay special attention to 3rd Generation Partnership Project (3GPP)
evolution in the area of network management and to the data that can be
extracted from 3GPP networks, in order to gain knowledge and experience in how
the network is working, and improve network performance in a proactive way.
Finally, we go through the main challenges associated with this line of
research, in both 4G and in what 5G is getting designed, while identifying new
directions for research.Comment: 23 pages, 3 figures, Surve
Model Checking Implantable Cardioverter Defibrillators
Ventricular Fibrillation is a disorganized electrical excitation of the heart
that results in inadequate blood flow to the body. It usually ends in death
within seconds. The most common way to treat the symptoms of fibrillation is to
implant a medical device, known as an Implantable Cardioverter Defibrillator
(ICD), in the patient's body. Model-based verification can supply rigorous
proofs of safety and efficacy. In this paper, we build a hybrid system model of
the human heart+ICD closed loop, and show it to be a STORMED system, a class of
o-minimal hybrid systems that admit finite bisimulations. In general, it may
not be possible to compute the bisimulation. We show that approximate
reachability can yield a finite simulation for STORMED systems, which improves
on the existing verification procedure. In the process, we show that certain
compositions respect the STORMED property. Thus it is possible to model check
important formal properties of ICDs in a closed loop with the heart, such as
delayed therapy, missed therapy, or inappropriately administered therapy. The
results of this paper are theoretical and motivate the creation of concrete
model checking procedures for STORMED systems.Comment: Hybrid Systems: Computation and Control 201
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks
Mobile networks possess information about the users as well as the network.
Such information is useful for making the network end-to-end visible and
intelligent. Big data analytics can efficiently analyze user and network
information, unearth meaningful insights with the help of machine learning
tools. Utilizing big data analytics and machine learning, this work contributes
in three ways. First, we utilize the call detail records (CDR) data to detect
anomalies in the network. For authentication and verification of anomalies, we
use k-means clustering, an unsupervised machine learning algorithm. Through
effective detection of anomalies, we can proceed to suitable design for
resource distribution as well as fault detection and avoidance. Second, we
prepare anomaly-free data by removing anomalous activities and train a neural
network model. By passing anomaly and anomaly-free data through this model, we
observe the effect of anomalous activities in training of the model and also
observe mean square error of anomaly and anomaly free data. Lastly, we use an
autoregressive integrated moving average (ARIMA) model to predict future
traffic for a user. Through simple visualization, we show that anomaly free
data better generalizes the learning models and performs better on prediction
task.Comment: IEEE Access Journal pape
A General Overview of Formal Languages for Individual-Based Modelling of Ecosystems
Various formal languages have been proposed in the literature for the
individual-based modelling of ecological systems. These languages differ in
their treatment of time and space. Each modelling language offers a distinct
view and techniques for analyzing systems. Most of the languages are based on
process calculi or P systems. In this article, we present a general overview of
the existing modelling languages based on process calculi. We also discuss,
briefly, other approaches such as P systems, cellular automata and Petri nets.
Finally, we show advantages and disadvantages of these modelling languages and
we propose some future research directions.Comment: arXiv admin note: text overlap with arXiv:1610.08171 by other author
Emerging whole-cell modeling principles and methods
Whole-cell computational models aim to predict cellular phenotypes from
genotype by representing the entire genome, the structure and concentration of
each molecular species, each molecular interaction, and the extracellular
environment. Whole-cell models have great potential to transform bioscience,
bioengineering, and medicine. However, numerous challenges remain to achieve
whole-cell models. Nevertheless, researchers are beginning to leverage recent
progress in measurement technology, bioinformatics, data sharing, rule-based
modeling, and multi-algorithmic simulation to build the first whole-cell
models. We anticipate that ongoing efforts to develop scalable whole-cell
modeling tools will enable dramatically more comprehensive and more accurate
models, including models of human cells.Comment: 10 pages, 2 figures, 7 supplementary table
Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes
This paper presents a comprehensive survey of existing authentication and
privacy-preserving schemes for 4G and 5G cellular networks. We start by
providing an overview of existing surveys that deal with 4G and 5G
communications, applications, standardization, and security. Then, we give a
classification of threat models in 4G and 5G cellular networks in four
categories, including, attacks against privacy, attacks against integrity,
attacks against availability, and attacks against authentication. We also
provide a classification of countermeasures into three types of categories,
including, cryptography methods, humans factors, and intrusion detection
methods. The countermeasures and informal and formal security analysis
techniques used by the authentication and privacy preserving schemes are
summarized in form of tables. Based on the categorization of the authentication
and privacy models, we classify these schemes in seven types, including,
handover authentication with privacy, mutual authentication with privacy, RFID
authentication with privacy, deniable authentication with privacy,
authentication with mutual anonymity, authentication and key agreement with
privacy, and three-factor authentication with privacy. In addition, we provide
a taxonomy and comparison of authentication and privacy-preserving schemes for
4G and 5G cellular networks in form of tables. Based on the current survey,
several recommendations for further research are discussed at the end of this
paper.Comment: 24 pages, 14 figure
- …