An experimental study of VBR video over various ATM switch architectures

One of the most important components of an Asynchronous Transfer Mode (ATM) network is the switch. Switch design is not a part of the ATM standards so vendors use a wide variety of techniques to build their switches. In this paper, the authors present experimental results of switching and multiplexing real-time Variable Bit Rate (VBR) video traffic (JPEG, MPEG-1, and MPEG-2) through two different ATM switch architectures. Real-time VBR traffic, such as digital video, is particularly interesting due to its high demands in terms of bandwidth, real-time delivery and processing requirements. The experiments show that the fastest switches, i.e., lowest latencies, do not necessarily perform better when transmitting VBR video. The impact of the high speed network components; characteristics, such as switch fabric architecture, buffering strategies, and higher layer transport protocols (i.e., UDP, TCP/IP), are illustrated through the experimental results

ATM network impairment to video quality

Includes bibliographical reference

DSTP-AN: A Distributed System for Transaction Processing Based on Data Resource Migration in ATM Networks

The dynamic migration of data resources has become a strong tool for transaction processing in broadband networks such as ATM. In this paper, a distributed system that takes advantage of data resource migration for transaction processing in ATM networks has been proposed. The proposed system provides mechanisms to select the transaction processing method, to migrate data resources in a way that reduces the time delay and message traffic in locating and accessing them. The first mechanism selects one of the two transaction processing methods: the traditional method that uses two phase commit protocol and other new method based on data resource migration. The second mechanism attempts to improve performance by making each site follow a local policy for directing requests to locate and access data resources as well as migrating them through the system. For this, a new scheme that focuses on reducing the time delay and message traffic needed to access the migratory data resources is proposed. The performance of the proposed scheme has also been evaluated and compared with one of the existing schemes by a simulation study under different system parameters such as frequency of access to the data resources, frequency of data resource migrations, scale of network, etc

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

User-centred and context-aware identity management in mobile ad-hoc networks

The emergent notion of ubiquitous computing makes it possible for mobile devices to communicate and provide services via networks connected in an ad-hoc manner. These have resulted in the proliferation of wireless technologies such as Mobile Ad-hoc Networks (MANets), which offer attractive solutions for services that need flexible setup as well as dynamic and low cost wireless connectivity. However, the growing trend outlined above also raises serious concerns over Identity Management (IM) due to a dramatic increase in identity theft. The problem is even greater in service-oriented architectures, where partial identities are sprinkled across many services and users have no control over such identities. In this thesis, we review some issues of contextual computing, its implications and usage within pervasive environments. To tackle the above problems, it is essential to allow users to have control over their own identities in MANet environments. So far, the development of such identity control remains a significant challenge for the research community. The main focus of this thesis is on the area of identity management in MANets and emergency situations by using context-awareness and user-centricity together with its security issues and implications. Context- awareness allows us to make use of partial identities as a way of user identity protection and node identification. User-centricity is aimed at putting users in control of their partial identities, policies and rules for privacy protection. These principles help us to propose an innovative, easy-to-use identity management framework for MANets. The framework makes the flow of partial identities explicit; gives users control over such identities based on their respective situations and contexts, and creates a balance between convenience and privacy. The thesis presents our proposed framework, its development and lab results/evaluations, and outlines possible future work to improve the framework