Ilir Murturi

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Verified

Ilir verified their affiliation via an institutional email.

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• Dr.
• PostDoc Position at TU Wien

In recent years, wireless sensor networks (WSNs) have become established as an effective way of interconnecting sensor devices with simultaneous communication and data processing. Since it allows low-cost and low-power sensor devices, the use of WSNs has increased in different application areas, such as environmental monitoring, smart cities and cr...

Software components within heterogeneous devices of the Internet of Things (IoT) systems use resources representing various computational capabilities, including sensing or actuation end points. However, components do not live in isolation and must be able to coordinate with others to fulfill their goals. Satisfaction of requirements-capturing thei...

In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud...

Recent advancements in distributed systems have enabled deploying low-latency edge applications (i.e., IoT applications) in proximity to the end-users, respectively, in edge networks. The stringent requirements combined with heterogeneous, resource-constrained and dynamic edge networks make the deployment process a challenging task. Besides that, t...

The increasing complexity of IoT applications and the continuous growth in data generated by connected devices have led to significant challenges in managing resources and meeting performance requirements in computing continuum architectures. Traditional cloud solutions struggle to handle the dynamic nature of these environments, where both infrast...

Deploying a Hierarchical Federated Learning (HFL) pipeline across the computing continuum (CC) requires careful organization of participants into a hierarchical structure with intermediate aggregation nodes between FL clients and the global FL server. This is challenging to achieve due to (i) cost constraints, (ii) varying data distributions, and (...

In this article, we explore the opportunities and benefits of integrating federated learning (FL) and blockchain technologies to build an adaptable and secure Knowledge-Defined Networking (KDN) system. Our aim is to enhance network performance by ensuring self-learning, self-adapting, and self-adjustment capabilities in dynamic and decentralized ne...

Authentication, authorization, and access control are fundamental functionalities that are crucial for network infrastructures and software applications. These functionalities work together to create a fundamental security layer that allows administrative entities to control user actions. Implementing a security layer may be simple for basic applic...

Distributed Computing Continuum Systems (DCCS) are integrated systems that combine cloud, edge, and IoT devices to deliver scalable and low-latency computing resources across diverse applications and environments. Composed of a heterogeneous mix of computational units, storage systems, and communication networks, DCCS facilitates real-time data pro...

Advanced wearable sensor devices have enabled the recording of vast amounts of movement data from individuals regarding their physical activities. This data offers valuable insights that enhance our understanding of how physical activities contribute to improved physical health and overall quality of life. Consequently, there is a growing need for...

Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes between clients and the global federated learning server in order to reduce communication costs and distribute server load. One side effect is that machine learning model replication at scale comes "for free" as part of the HFL process: model replicas are hosted a...

The proliferation of the Internet of Things (IoT) and the advancements in machine learning (ML) have facilitated ubiquitous sensing and computing capabilities, enabling the interconnection of a wide array of devices to the Internet. Traditionally, data collection and data processing have been centralized, which may not be feasible due to issues suc...

With the rapid advancement of artificial intelligence (AI), the proliferation of deep neural networks (DNNs) has ushered in a transformative era, revolutionizing modern lifestyles and enhancing production efficiency. However, the substantial computational and data requirements generated by Internet of Things (IoT) devices present a significant bott...

Internet of Things (IoT) devices pose significant security challenges due to their heterogeneity (i.e., hardware and software) and vulnerability to extensive attack surfaces. Today's conventional perimeter-based systems use credential-based authentication (e.g., username/password, certificates, etc.) to decide whether an actor can access a network....

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and...

As a driving force in the advancement of intelligent in-orbit applications, DNN models have been gradually integrated into satellites, producing daily latency-constraint and computation-intensive tasks. However, the substantial computation capability of DNN models, coupled with the instability of the satellite-ground link, pose significant challeng...

Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc.). At the same time, implement...

Federated Learning (FL) has emerged as a promising paradigm to train machine learning models collaboratively while preserving data privacy. However, its widespread adoption faces several challenges, including scalability, heterogeneous data and devices, resource constraints, and security concerns. Despite its promise, FL has not been specifically a...

Computing paradigms have evolved significantly in recent decades, moving from large room-sized resources (processors and memory) to incredibly small computing nodes. Recently, the power of computing has attracted almost all current application fields. Currently, distributed computing continuum systems (DCCSs) are unleashing the era of a computing p...

Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data...

Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. However, compared with conventional mobile cloud computing, ubiquitous MEC intr...

Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The d...

In recent years, visualizing high-quality 3D content within modern applications (e.g., Augmented or Virtual Reality) is increasingly being generated procedurally rather than explicitly. This manifests in producing highly detailed geometries entailing resource-intensive computational workloads (i.e., Procedural Geometry Workloads) with particular ch...

Edge intelligence and, by extension, any distributed computing continuum system will bring to our future society a plethora of new and useful applications, which will certainly revolutionize our way of living. Nevertheless, managing these systems challenges all previously developed technologies for Internet-distributed systems. In this regard, this...

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imper...

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this article presents a...

Internet distributed systems are subjected to a new transformation thanks to the success of Cloud Computing.

The convergence between AI planning techniques and the Internet of Things (IoT) can solve various operational and business challenges. However, IoT systems' stringent requirements such as latency and scalability have introduced several challenges to execute and scale planners in cloud environments. Edge computers placed close to the IoT domain (e.g...

The growing number of Internet of Things (IoT) devices generates massive amounts of diverse data, including personal or confidential information (i.e., sensory, images, etc.) that is not intended for public view. Traditionally, predefined privacy policies are usually enforced in resource-rich environments such as the cloud to protect sensitive info...

Recent advancements in distributed systems have enabled deploying low-latency and highly resilient edge applications close to the IoT domain at the edge of the network. The broad range of edge application requirements combined with heterogeneous, resource-constrained, and dynamic edge networks make it particularly challenging to conigure and deploy...

The growing number of Internet of Things (IoT) devices generates massive amounts of diverse data, including personal or confidential information (i.e., sensory, images, etc.) that is not intended for public view. Traditionally, predefined privacy policies are usually enforced in resource-rich environments such as the cloud to protect sensitive info...

Contemporary applications such as those within Augmented or Virtual Reality (AR/VR) pose challenges for software architectures supporting them, which have to adhere to stringent latency, data transmission, and performance requirements. This manifests in processing 3D models, whose 3D contents are increasingly generated procedurally rather than expl...

Edge computing is a fundamental enabler for the proliferation of the Internet of Things (IoT). Resources, including compute and storage, are increasingly located at the edge of the network and bridge the gap between the cloud and IoT entities. Edge computing enables low-latency, privacy-awareness, and resilient applications. Many of the application...

In mathematical statistics, an interesting and common problem is finding the best linear or non-linear regression equations that express the relationship between variables or data. The method of least squares (MLS) represents one of the oldest procedures among multiple techniques to determine the best fit line to the given data through simple calcu...

In today's IoT infrastructures, increasingly newly added computational resources at the edge of a network are added to acquire faster response and increased privacy. Such edge networks bring an opportunity for deploying edge application services in proximity to IoT domains and the end-users. In this paper, we consider the problem of utilizing vario...

With the success of the Internet of Things (IoT) and the widespread availability of mobile devices, the traditional centralized cloud computing is facing severe network challenges (e.g. high latency, bandwidth cost). These challenges prove that the current approach is insufficient to meet the rigorous requirements of IoT applications. Besides the n...

Engineering Internet of Things (IoT) systems is a challenging task partly due to the dynamicity and uncertainty of the environment including the involvement of the human in the loop. Users should be able to achieve their goals seamlessly in different environments, and IoT systems should be able to cope with dynamic changes. Several approaches have...

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this p...

Edge computing has been recently introduced as an intermediary between Internet of Things (loT) deployments and the cloud, providing data or control facilities to participating loT devices. This includes actively supporting loT resource discovery, something particularly pertinent when building large-scale, distributed and heterogeneous loT systems....

Recently crowdsourcing is being established as the new platform for capturing ideas from multiple users, i.e., the crowd. Many companies have already shifted their approach towards utilising the power of the crowd. Transparency and quality of election process is the main factor for acknowledging the general election results. Voters, crowd feedback...

The face recognition applications deal with large amounts of images and remain difficult to accomplish due to when displayed with images taken in unlimited conditions. Linear discriminant analysis (LDA) is a supervised method that uses training samples to obtain the projection matrix for feature extraction, while deep neural networks are trainable...

The face recognition applications deal with large amounts of images and remain difficult to accomplish due to when displayed with images taken in unlimited conditions. Linear Discriminant Analysis (LDA) is a supervised method that uses training samples to obtain the projection matrix for feature extraction, while deep neural networks are trainable...

This work deals with the stability analysis of two-legged (humanoid) robots during walking. First, a brief overview is provided on biped robots, and also models for the dynamic behavior are discussed. There is currently the low base of robot - consisting of feet, legs, hips and upper part of robots body. This structure currently has seven degrees o...

Quality of election process is the main factor for acknowledging the general election results. In this sense a feedback from voters is critical to maintain a desired quality of the process. Crowdsourcing is establishing as a standard platform for capturing feedback and new ideas from the participating stakeholders. This paper presents an efficient...