A bibliometric analysis and review of resource management in internet of water things : the use of game theory

DATA AVAILABILITY STATEMENT : The data presented in this study are openly available in Mendeley Data at doi:10.17632/2wxgbcxn3t.1.To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.The University of Pretoria.https://www.mdpi.com/journal/wateram2023Electrical, Electronic and Computer Engineerin

Game-Theoretic Foundations for Forming Trusted Coalitions of Multi-Cloud Services in the Presence of Active and Passive Attacks

The prominence of cloud computing as a common paradigm for offering Web-based services has led to an unprecedented proliferation in the number of services that are deployed in cloud data centers. In parallel, services' communities and cloud federations have gained an increasing interest in the recent past years due to their ability to facilitate the discovery, composition, and resource scaling issues in large-scale services' markets. The problem is that the existing community and federation formation solutions deal with services as traditional software systems and overlook the fact that these services are often being offered as part of the cloud computing technology, which poses additional challenges at the architectural, business, and security levels. The motivation of this thesis stems from four main observations/research gaps that we have drawn through our literature reviews and/or experiments, which are: (1) leading cloud services such as Google and Amazon do not have incentives to group themselves into communities/federations using the existing community/federation formation solutions; (2) it is quite difficult to find a central entity that can manage the community/federation formation process in a multi-cloud environment; (3) if we allow services to rationally select their communities/federations without considering their trust relationships, these services might have incentives to structure themselves into communities/federations consisting of a large number of malicious services; and (4) the existing intrusion detection solutions in the domain of cloud computing are still ineffective in capturing advanced multi-type distributed attacks initiated by communities/federations of attackers since they overlook the attacker's strategies in their design and ignore the cloud system's resource constraints. This thesis aims to address these gaps by (1) proposing a business-oriented community formation model that accounts for the business potential of the services in the formation process to motivate the participation of services of all business capabilities, (2) introducing an inter-cloud trust framework that allows services deployed in one or disparate cloud centers to build credible trust relationships toward each other, while overcoming the collusion attacks that occur to mislead trust results even in extreme cases wherein attackers form the majority, (3) designing a trust-based game theoretical model that enables services to distributively form trustworthy multi-cloud communities wherein the number of malicious services is minimal, (4) proposing an intra-cloud trust framework that allows the cloud system to build credible trust relationships toward the guest Virtual Machines (VMs) running cloud-based services using objective and subjective trust sources, (5) designing and solving a trust-based maxmin game theoretical model that allows the cloud system to optimally distribute the detection load among VMs within a limited budget of resources, while considering Distributed Denial of Service (DDoS) attacks as a practical scenario, and (6) putting forward a resource-aware comprehensive detection and prevention system that is able to capture and prevent advanced simultaneous multi-type attacks within a limited amount of resources. We conclude the thesis by uncovering some persisting research gaps that need further study and investigation in the future

New Perspectives on Modelling and Control for Next Generation Intelligent Transport Systems

This PhD thesis contains 3 major application areas all within an Intelligent Transportation System context. The first problem we discuss considers models that make beneficial use of the large amounts of data generated in the context of traffic systems. We use a Markov chain model to do this, where important data can be taken into account in an aggregate form. The Markovian model is simple and allows for fast computation, even on low end computers, while at the same time allowing meaningful insight into a variety of traffic system related issues. This allows us to both model and enable the control of aggregate, macroscopic features of traffic networks. We then discuss three application areas for this model: the modelling of congestion, emissions, and the dissipation of energy in electric vehicles. The second problem we discuss is the control of pollution emissions in eets of hybrid vehicles. We consider parallel hybrids that have two power units, an internal combustion engine and an electric motor. We propose a scheme in which we can in uence the mix of the two engines in each car based on simple broadcast signals from a central infrastructure. The infrastructure monitors pollution levels and can thus make the vehicles react to its changes. This leads to a context aware system that can be used to avoid pollution peaks, yet does not restrict drivers unnecessarily. In this context we also discuss technical constraints that have to be taken into account in the design of traffic control algorithms that are of a microscopic nature, i.e. they affect the operation of individual vehicles. We also investigate ideas on decentralised trading of emissions. The goal here is to allocate the rights to pollute fairly among the eet's vehicles. Lastly we discuss the usage of decentralised stochastic assignment strategies in traffic applications. Systems are considered in which reservation schemes can not reliably be provided or enforced and there is a signifficant delay between decisions and their effect. In particular, our approach facilitates taking into account the feedback induced into traffic systems by providing forecasts to large groups of users. This feedback can invalidate the predictions if not modelled carefully. At the same time our proposed strategies are simple rules that are easy to follow, easy to accept, and significantly improve the performance of the systems under study. We apply this approach to three application areas, the assignment of electric vehicles to charging stations, the assignment of vehicles to parking facilities, and the assignment of customers to bike sharing stations. All discussed approaches are analysed using mathematical tools and validated through extensive simulations

A Stackelberg security game with random strategies based on the extraproximal theoretic approach

In this paper we present a novel approach for representing a real-world attacker defender Stackelberg security game- theoretic model based on the extraproximal method. We focus on a class of ergodic controlled finite Markov chain games. The extraproximal problem formulation isc onsidered as a nonlinear programming problem withr espect to stationary distributions. The Lagrange principle and Tikhonov's regularization method are employed to ensure the convergence of the costfunctions.We transform the problem into a system of equations in aproximal format, and a two-step (prediction and basic) iterated procedure is applied to solve the formulated problem.In particular, the extraproximal method is employed for computing mixed strategies, providing a strong optimization formulation to compute the Stackelberg/Nas hequilibrium. Mixed strategies are especially found when the resources available for both the defender and the attacker are limited. In this sense,each equation in this system is an optimization problem for which the minimum is found using aquadratic programming approach.The model supports a defenderand N attackers. In order to address the dynamic execution uncertainty in security patrolling, we provide a game theoretic based method fo rscheduling randomized patrols. Simulation results provide avalidations of our approach