1,500 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Ensuring Average Recovery with Adversarial Scheduler
In this paper, we focus on revising a given program so that the average recovery time in the presence of an adversarial scheduler is bounded by a given threshold lambda. Specifically, we consider the scenario where the fault (or other unexpected action) perturbs the program to a state that is outside its set of legitimate states. Starting from this state, the program executes its actions/transitions to recover to legitimate states. However, the adversarial scheduler can force the program to reach one illegitimate state that requires a longer recovery time.
To ensure that the average recovery time is less than lambda, we need to remove certain transitions/behaviors. We show that achieving this average response time while removing minimum transitions is NP-hard. In other words, there is a tradeoff between the time taken to synthesize the program and the transitions preserved to reduce the average convergence time. We present six different heuristics and evaluate this tradeoff with case studies. Finally, we note that the average convergence time considered here requires formalization of hyperproperties. Hence, this work also demonstrates feasibility of adding (certain) hyperproperties to an existing program
Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey
Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent
Transportation Systems (ITS) by facilitating communication between vehicles and
infrastructure. This communication aims to enhance road safety, improve traffic
efficiency, and enhance passenger comfort. The secure and reliable exchange of
information is paramount to ensure the integrity and confidentiality of data,
while the authentication of vehicles and messages is essential to prevent
unauthorized access and malicious activities. This survey paper presents a
comprehensive analysis of existing authentication mechanisms proposed for
cluster-based VANETs. The strengths, weaknesses, and suitability of these
mechanisms for various scenarios are carefully examined. Additionally, the
integration of secure key management techniques is discussed to enhance the
overall authentication process. Cluster-based VANETs are formed by dividing the
network into smaller groups or clusters, with designated cluster heads
comprising one or more vehicles. Furthermore, this paper identifies gaps in the
existing literature through an exploration of previous surveys. Several schemes
based on different methods are critically evaluated, considering factors such
as throughput, detection rate, security, packet delivery ratio, and end-to-end
delay. To provide optimal solutions for authentication in cluster-based VANETs,
this paper highlights AI- and ML-based routing-based schemes. These approaches
leverage artificial intelligence and machine learning techniques to enhance
authentication within the cluster-based VANET network. Finally, this paper
explores the open research challenges that exist in the realm of authentication
for cluster-based Vehicular Adhoc Networks, shedding light on areas that
require further investigation and development
Consensus-based control for a network of diffusion PDEs with boundary local interaction
In this paper the problem of driving the state of a network of identical
agents, modeled by boundary-controlled heat equations, towards a common
steady-state profile is addressed. Decentralized consensus protocols are
proposed to address two distinct problems. The first problem is that of
steering the states of all agents towards the same constant steady-state
profile which corresponds to the spatial average of the agents initial
condition. A linear local interaction rule addressing this requirement is
given. The second problem deals with the case where the controlled boundaries
of the agents dynamics are corrupted by additive persistent disturbances. To
achieve synchronization between agents, while completely rejecting the effect
of the boundary disturbances, a nonlinear sliding-mode based consensus protocol
is proposed. Performance of the proposed local interaction rules are analyzed
by applying a Lyapunov-based approach. Simulation results are presented to
support the effectiveness of the proposed algorithms
Stability and stabilization of sampled-data control for lure systems
Este trabalho apresenta um novo método para a análise de estabilidade e estabilização de sistemas do tipo Lure com controle amostrado, sujeitos a amostragem aperiódica e não linearidades que são limitadas em setor e restritas em derivada, em ambos contextos global e regional. Assume-se que os estados da planta estão disponíveis para medição e que as não linearidades são conhecidas, o que leva a uma formulação mais geral do problema. Os estados são adquiridos por um controlador digital que atualiza a entrada de controle em instantes de tempo discretos e aperiódicos, mantendo-a constante entre dois instantes sucessivos de amostragem. A abordagem apresentada neste trabalho é baseada no uso de uma nova classe de looped-functionals e em uma função do tipo Lure generalizada, que leva a condições de estabilidade e estabilização que são escritas na forma de desigualdades matriciais lineares (LMIs) e quasi-LMIs, respectivamente. Com base nestas condições, problemas de otimização são formulados com o objetivo de computar o intervalo máximo entre amostragens ou os limites máximos do setor para os quais a estabilidade assintótica da origem do sistema de dados amostrados em malha fechada é garantida. No caso em que as condições de setor são válidas apenas localmente, a solução desses problemas também fornece uma estimativa da região de atração para as trajetórias em tempo contínuo do sistema em malha fechada. Como as condições de síntese são quasi-LMIs, um algoritmo de otimização por enxame de partículas é proposto para lidar com as não linearidades envolvidas nos problemas de otimização, que surgem do produto de algumas variáveis de decisão. Exemplos numéricos são apresentados ao longo do trabalho para destacar as potencialidades do método.This work presents a new method for stability analysis and stabilization of sampleddata controlled Lure systems, subject to aperiodic sampling and nonlinearities that are sector bounded and slope restricted, in both global and regional contexts. We assume that the states of the plant are available for measurement and that the nonlinearities are known, which leads to a more general formulation of the problem. The states are acquired by a digital controller which updates the control input at aperiodic discrete-time instants, keeping it constant between successive sampling instants. The approach here presented is based on the use of a new class of looped-functionals and a generalized Luretype function, which leads to stability and stabilization conditions that are written in the form of Linear Matrix Inequalities (LMIs) and quasi-LMIs, respectively. On this basis, optimization problems are formulated aiming to compute the maximal intersampling interval or the maximal sector bounds for which the asymptotic stability of the origin of the sampled-data closed-loop system is guaranteed. In the case where the sector conditions hold only locally, the solution of these problems also provide an estimate of the region of attraction for the continuous-time trajectories of the closed-loop system. As the synthesis conditions are quasi-LMIs, a Particle Swarm Optimization (PSO) algorithm is proposed to deal with the involved nonlinearities in the optimization problems, which arise from the product of some decision variables. Numerical examples are presented throughout the work to highlight the potentialities of the method
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