With the increasing adoption of AI applications such as large language models and computer vision AI, the computational demands on AI inference systems are continuously rising, making the enhancement of task processing capacity using existing hardware a primary objective in edge clouds. We propose EPARA, an end-to-end AI parallel inference framework in edge, aimed at enhancing the edge AI serving capability. Our key idea is to categorize tasks based on their sensitivity to latency/frequency and requirement for GPU resources, thereby achieving both request-level and service-level task-resource allocation. EPARA consists of three core components: 1) a task-categorized parallelism allocator that decides the parallel mode of each task, 2) a distributed request handler that performs the calculation for the specific request, and 3) a state-aware scheduler that periodically updates service placement in edge clouds. We implement a EPARA prototype and conduct a case study on the EPARA operation for LLMs and segmentation tasks. Evaluation through testbed experiments involving edge servers, embedded devices, and microcomputers shows that EPARA achieves up to 2.1$\times$ higher goodput in production workloads compared to prior frameworks, while adapting to various edge AI inference tasks.

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach.

The fifth generation (5G) cellular network technology is mature and increasingly utilized in many industrial and robotics applications, while an important functionality is the advanced Quality of Service (QoS) features. Despite the prevalence of 5G QoS discussions in the related literature, there is a notable absence of real-life implementations and studies concerning their application in time-critical robotics scenarios. This article considers the operation of time-critical applications for 5G-enabled unmanned aerial vehicles (UAVs) and how their operation can be improved by the possibility to dynamically switch between QoS data flows with different priorities. As such, we introduce a robotics oriented analysis on the impact of the 5G QoS functionality on the performance of 5G-enabled UAVs. Furthermore, we introduce a novel framework for the dynamic selection of distinct 5G QoS data flows that is autonomously managed by the 5G-enabled UAV. This problem is addressed in a novel feedback loop fashion utilizing a probabilistic finite state machine (PFSM). Finally, the efficacy of the proposed scheme is experimentally validated with a 5G-enabled UAV in a real-world 5G stand-alone (SA) network.

In this paper an autonomous system to detect and combat Rumex obtusifolius leveraging autonomous unmanned aerial vehicles (UAV), small autonomous sprayer robots and 5G SA connectivity is presented. Rumex obtusifolius is a plant found on grassland that drains nutrients from surrounding plants and has lower nutritive value than the surrounding grass. High concentrations of it have to be combated in order to use the grass as feed for livestock. One or more UAV are controlled through 5G to survey the current working area and send back high-definition photos of the ground to an edge cloud server. There an AI algorithm using neural networks detects the Rumex obtusifolius and calculates its position using the UAVs position data. When plants are detected an optimal path is calculated and sent via 5G to the sprayer robot to get to them in minimal time. It will then move to the position of the broad-leafed dock and use an on-board camera and the edge cloud to verify the position of the plant and precisely spray crop protection only where the target plant is. The spraying robot and UAV are already operational, the training of the detection algorithm is still ongoing. The described system is being tested with a fixed private 5G SA network and a nomadic 5G SA network as public cellular networks are not performant enough in regards to low latency and upload bandwidth.

As new technology is constantly being developed, trends are merged and combined to enhance functionality and improve old systems. The future of fifth-generation cellular technology and artificial intelligence is a perfect example of how today's innovators can apply two separate concepts together to develop new use cases and refine the inventions of the past to better serve the needs of the future. Read on to learn more about how 5G and AI technology will work together to produce exciting new developments. With their powers combined, AI and 5G technologies are a superforce. This dynamic duo has the potential to transform many different industries -- from healthcare and transportation to entertainment and beyond.

For example, environmental science studies habitat borders where certain plant varieties grow strongly at the edge and not further. Similar phenomena have been observed in astronomy at the edges of the universe. Human societies are no exception. A new revolution is underway with high computing powers that move to the edge, a phenomenon becoming increasingly known as Edge Computing. IoT is about collecting and analyzing data, insights, automation, and automating processes that involve machines, people, things, places, and other objects.

Gartner's list of the most comprehensive trends that CIOs and other senior executives should be paying attention to for 2021 includes "people-centricity," location independence, and resilient delivery. At its IT Symposium/Xpo conference every year, Gartner produces one of the most comprehensive lists of the trends that CIOs and other senior executives should be paying attention to. This year, Gartner vice president Brian Burke presented the Top 10 strategic technology trends for 2021, grouping the main trends into "people centricity," "location independence," and "resilient delivery." People centricity carries over from last year's list but you can certainly see the increased emphasis on location independence and resilient delivery as a reaction to the pandemic, something no one saw a year ago. Burke said 2020 has seen huge upheaval driven by of the pandemic and its related economic impacts, and this sets the stage for a major change in the IT landscape.

As video streaming, Alexa-type digital assistants and self-driving cars continue to permeate daily life, edge computing architecture has become foundational to enable these tasks. These data-intensive processes are fueled by a proliferation of Internet of Things (IoT) devices.. According to Statista, there will be 30.9 billion devices by 2025. These devices are becoming increasingly intelligent as well, with more analytics and decision-making capabilities at the device level. "There are more and more devices that need intelligent capabilities, especially to process AI at the edge," said Aditya Kaul, research director at Omdia.

With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.

The edge of a network, as you may know, is the furthest extent of its reach. A cloud platform is a kind of network overlay that makes multiple network locations part of a single network domain. It should therefore stand to reason that an edge cloud is a single addressable, logical network at the furthest extent of a physical network. And an edge cloud on a global scale should be a way to make multiple, remote data centers accessible as a single pool of resources -- of processors, storage, and bandwidth. The combination of 5G and edge computing will unleash new capabilities from real-time analytics to automation to self-driving cars and trucks.