19,095 research outputs found
An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks
Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful
energy awareness is essential when working with these devices.
Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features.
This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols.
The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and
has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference
publications in IEEE Explore and one workshop paper
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
GADGET: A code for collisionless and gasdynamical cosmological simulations
We describe the newly written code GADGET which is suitable both for
cosmological simulations of structure formation and for the simulation of
interacting galaxies. GADGET evolves self-gravitating collisionless fluids with
the traditional N-body approach, and a collisional gas by smoothed particle
hydrodynamics. Along with the serial version of the code, we discuss a parallel
version that has been designed to run on massively parallel supercomputers with
distributed memory. While both versions use a tree algorithm to compute
gravitational forces, the serial version of GADGET can optionally employ the
special-purpose hardware GRAPE instead of the tree. Periodic boundary
conditions are supported by means of an Ewald summation technique. The code
uses individual and adaptive timesteps for all particles, and it combines this
with a scheme for dynamic tree updates. Due to its Lagrangian nature, GADGET
thus allows a very large dynamic range to be bridged, both in space and time.
So far, GADGET has been successfully used to run simulations with up to 7.5e7
particles, including cosmological studies of large-scale structure formation,
high-resolution simulations of the formation of clusters of galaxies, as well
as workstation-sized problems of interacting galaxies. In this study, we detail
the numerical algorithms employed, and show various tests of the code. We
publically release both the serial and the massively parallel version of the
code.Comment: 32 pages, 14 figures, replaced to match published version in New
Astronomy. For download of the code, see
http://www.mpa-garching.mpg.de/gadget (new version 1.1 available
Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework
The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks—a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay.
In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR®, a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies’ data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a self-optimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal
Penalized flexible Bayesian quantile regression
Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to en- able exact inference and shrinkage of an unimportant coefficient to zero. The error distribution is assumed to be an infi- nite mixture of Gaussian densities. We have theoretically investigated and numerically compared our proposed methods with Flexible Bayesian quantile regression (FBQR), Lasso quantile regression (LQR) and quantile regression (QR) methods. Simulations and real data studies are conducted under different settings to assess the performance of the pro- posed methods. The proposed methods perform well in comparison to the other methods in terms of median mean squared error, mean and variance of the absolute correlation criterions. We believe that the proposed methods are useful practically
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