2,182 research outputs found
Secure and robust multi-constrained QoS aware routing algorithm for VANETs
Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach
In this paper, we aim to design sparse D-optimal (determinantoptimal)
pose-graph SLAM problems through the synthesis of sparse graphs with the
maximum weighted number of spanning trees. Characterizing graphs with the
maximum number of spanning trees is an open problem in general. To tackle this
problem, several new theoretical results are established in this paper,
including the monotone log-submodularity of the weighted number of spanning
trees. By exploiting these structures, we design a complementary pair of
near-optimal efficient approximation algorithms with provable guarantees. Our
theoretical results are validated using random graphs and a publicly available
pose-graph SLAM dataset.Comment: WAFR 201
Conceptualizing a Multi-Sided Platform for Cloud Computing Resource Trading
Cost-effective and responsible use of cloud computing resources (CCR) is on the business agenda of companies of all sizes. Despite this strategic goal, a typical data center produces an estimated 30% overcapacity annually. This overcapacity has severe economic and environmental consequences. Our work addresses this overcapacity by proposing a multi-sided platform for CCR trading. We initiate our research by conducting a literature review to explore the existing body of knowledge which indicates a lack of recent and evaluated platform design knowledge for CCR trading. We address this research gap by deriving and evaluating design requirements and design principles. We instantiate and evaluate the design knowledge in a respective platform framework. Thus, we contribute to research and practice by deriving and evaluating design knowledge and proposing an evaluated platform framework
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