Zeinab Nezami

Blockchain and edge computing are two instrumental paradigms of decentralized computation, driving key advancements in smart cities applications, such as supply chain, energy and mobility. Despite their unprecedented impact on society, they remain significantly fragmented as technologies and research areas, while they share fundamental principles of distributed systems and domains of applicability. This paper introduces a novel and large-scale systematic literature review on the nexus of blockchain and edge computing with the aim to unravel a new understanding of how the interfacing of the two computing paradigms can boost innovation to provide solutions to timely but also long-standing research challenges. By collecting almost 6000 papers from 3 databases and putting under scrutiny almost 1000 papers, we build a novel taxonomy and classification consisting of 22 features with 287 attributes that we study using quantitative and machine learning methods. They cover a broad spectrum of technological, design, epistemological and sustainability aspects. Results reveal 4 distinguishing patterns of interplay between blockchain and edge computing with key determinants the public (permissionless) vs. private (permissioned) design, technology and proof of concepts. They also demonstrate the prevalence of blockchain-assisted edge computing for improving privacy and security, in particular for mobile computing applications.

The rise of Large Language Models (LLMs) has transformed natural language processing (NLP) and generative AI applications. However, deploying these transformer-based models in resource-constrained environments poses a significant challenge due to their high computational and memory demands. To address this, we introduce in this paper GenAI on the Edge, a comprehensive benchmarking dataset designed to evaluate the performance of LLMs deployed on edge devices. Leveraging a distributed testbed of Raspberry Pi 5 devices orchestrated with lightweight Kubernetes (K3s), the dataset captures a broad range of performance metrics essential for assessing the feasibility of local inference in constrained environments. These metrics include detailed measurements of throughput, inference latency, memory utilization, and computational efficiency, along with granular timing data for key stages of the inference pipeline – Sample, Prefill, and Decode phases. We systematically evaluate LLMs of varying sizes under real-world deployment scenarios, with a particular emphasis on CPU-based edge platforms. By conducting multiple runs of conversation-based evaluations, GenAI on the Edge provides actionable insights into the trade-offs between performance and resource efficiency, enabling better decision-making for LLM deployment in edge environments.

The transformation of smart mobility is unprecedented–Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic patterns and requirements for real-time computation at large-scale, for instance, self-driving cars and augmented reality applications. The cloud computing paradigm can neither respond to such low-latency requirements nor adapt resource allocation to such dynamic spatio-temporal service requests. This paper addresses this grand challenge by introducing a novel decentralized optimization framework for mobility-aware edge-to-cloud resource allocation, service offloading, provisioning and load-balancing. In contrast to related work, this framework comes with superior efficiency and cost-effectiveness under evaluation in real-world traffic settings and mobility datasets. This breakthrough capability of ‘computing follows vehicles’ proves to be superior to balance resource utilization in a highly cost-effective way, while preventing service deadline violations by 14%-34%.

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

We are facing a huge revolution called the Internet of Things/Internet of Everything (IoT/IoE), which will enable pervasive computing scenarios. The IoT/IoE is expanding throughout the world at an incredible speed. Although this rapid development has created new opportunit ies, it has also broadened the range of people's expectations. However, the IoT/IoE is still in its infancy, and, therefore, its future structure and ingredients must be further studied and outlined.