Stable Throughput and Delay Analysis of a Random Access Network With Queue-Aware Transmission

In this work we consider a two-user and a three-user slotted ALOHA network with multi-packet reception (MPR) capabilities. The nodes can adapt their transmission probabilities and their transmission parameters based on the status of the other nodes. Each user has external bursty arrivals that are stored in their infinite capacity queues. For the two- and the three-user cases we obtain the stability region of the system. For the two-user case we provide the conditions where the stability region is a convex set. We perform a detailed mathematical analysis in order to study the queueing delay by formulating two boundary value problems (a Dirichlet and a Riemann-Hilbert boundary value problem), the solution of which provides the generating function of the joint stationary probability distribution of the queue size at user nodes. Furthermore, for the two-user symmetric case with MPR we obtain a lower and an upper bound for the average delay without explicitly computing the generating function for the stationary joint queue length distribution. The bounds as it is seen in the numerical results appear to be tight. Explicit expressions for the average delay are obtained for the symmetrical model with capture effect which is a subclass of MPR models. We also provide the optimal transmission probability in closed form expression that minimizes the average delay in the symmetric capture case. Finally, we evaluate numerically the presented theoretical results.Comment: Submitted for journal publicatio

Research, Evaluation and Sustainability: Reflecting on 15 Years of Experience in eHealth

In this paper we reflect on 15 years of experience in eHealth research and evaluation in the UK. An integrated, interroperable eHealth programme has yet to materialise. The initial promise that eHealth interventions can facilitate the shaping of the 21st century healthcare systems Stead et al. [1], has not been empirically demonstrated in the past decade. Although isolated cases of successful initiatives exist, the potential of eHealth remains largely unrealised Muuraiskangas et al. [2-4]. Investment in large programmes which aim to develop an integrated and interoperable platform across settings and levels of care, such as the NPfIT in the UK, may not have provided good value for money. Worse still, patient’s safety may have also been compromised Han et al. [5]. Similarly, to eCommerce, eGovernment and eLearning, eHealth (Table 1) was the result of both a demand by consumers and an initiative by government. It was therefore initially considered a paradox that it failed to deliver the much anticipated benefits. a paradox that it failed to deliver the muchanticipated benefits. A closer study of the various implementation programmes reveals the reasons for the relative failure of the UK to have a homogeneous and interoperable eHealth platform

Allocation of Heterogeneous Resources of an IoT Device to Flexible Services

Internet of Things (IoT) devices can be equipped with multiple heterogeneous network interfaces. An overwhelmingly large amount of services may demand some or all of these interfaces' available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds. For reasonable instance sizes, the presented formulation produces optimal solutions for this computationally hard problem. We prove the NP-Completeness of the problem and develop two algorithms to approximate the optimal solution for big instance sizes. The first algorithm allocates the most demanding service requirements first, considering the average cost of interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally demanding shares. Finally, we provide simulation results giving insight into services splitting over different interfaces for both cases.Comment: IEEE Internet of Things Journa

Neural correlates of consciousness in the complexity of brain networks

How do we define consciousness? Besides philosophical endeavours, the development of modern neuroimaging techniques fostered a principled way of quantifying the neural correlates of consciousness. Acquiring and analysing resting-state functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data, has allowed neuroscientists to noninvasively map the brain’s functional interactions (or functional connectivity). Based on data obtained during controlled loss of consciousness and in cases of patients with disorders of consciousness, it has now been suggested that multiple, functionally specialized/segregated areas need to interact and integrate information in order to support consciousness. Thus an emerging idea in neuroscience is that the brain needs to balance the coexistence of functional segregation and integration, a property often termed as brain complexity, in order to produce consciousness. A resulting hypothesis is that consciousness is abolished when the balance between segregation and integration is lost and brain complexity is attenuated. In that regard, I use complexity of functional connectivity, an aggregate measure of segregation and integration, as a marker of consciousness. This effort consists of two parts. First, I provide evidence that complexity in the healthy, awake brain is critical in the sense that it reflects a critical balance of segregation and integration designed to support efficient information communication. In turn, I provide evidence that loss of consciousness is associated with decreased complexity i.e. that functional connectivity departs from the critical complexity of the healthy, awake brain towards a more segregated configuration. The structure of this thesis follows accordingly. In the first experimental chapter (3), I show the importance of the critical balance of complexity in the healthy, awake brain by using a structure-to-function association model. Specifically, I show that complexity can be derived upon certain optimal, structural connections (computed as the Nash equilibrium between regions), which promote efficient communication in the brain from the regional to the whole-brain level. Chapter 4 focuses on capturing alterations of complexity in cases of sedation, anaesthesia and disorders of consciousness. Specifically, I show that as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness, functional connectivity becomes less complex and more segregated. A refined approach that quantifies complexity in different parts of the brain allowed me to see whether this reduction in complexity is more evident in specific regions and networks. Under this framework, at the regional level I provide evidence that sparsely connected regions linking different parts of the brain play a critical role in whole-brain complexity. At the network level I show the importance of the default mode network in whole-brain complexity. Even during rest, the brain is not static and displays rich temporal dynamics. Thus it is not only the complexity at each snapshot of time but also how complexity changes across time that can help us understand loss of consciousness. In chapter 5 I use a dynamic framework to derive and characterize the dynamics of functional connectivity during loss of consciousness. In turn, I provide evidence that brains become less temporally complex as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness. Moreover, my goal is to see whether the principle of complexity reduction can be also applied to the developing brain. Towards this direction, in chapter 6 I use complexity on EEG connectivity data to examine anaesthetic-induced loss of consciousness in infants. Specifically, I show that complexity in anaesthetised infants aged 0-3 years is reduced compared to a state of emergence from anaesthesia, indicating its importance in supporting consciousness and brain function since infancy. Taken together, these findings show that while the complexity of the healthy, awake brain during rest is critically configured, the unconscious brain is characterized by reduced complexity. Based on the results presented in this work, I propose that consciousness can be assessed on the basis of complexity of resting-state functional connectivity data