The journey from 5G towards 6G

This paper gives an overview of the journey from 5G towards 6G evolution. The 5G has been built across three main application verticals as defined by ITU, namely: Enhanced Mobile Broadband, Massive Machine Type Communications and Ultra-reliable Low Latency Communications (URRLC). To support these verticals, 5G has defined the following enablers: Massive MIMO, cloudification of network infrastructure, network automation, network slicing and edge cloud computing. It is expected that 5G will provide flexibility in terms of openness, mobility, programmability and agility and robustness in a standardized manner. The journey towards 6G will describe the limitations of 5G technologies and outlines the technology enablers for 6G. These enablers include smooth integration and interworking of Non-Terrestrial Networking technologies (NTN), use of Reconfigurable Intelligent Surfaces (RIS) and use of AI to orchestrate network and cloud resources. Additionally, the paper will give an overview of 6G research initiatives at both regional and international level

A Generic Framework for Deploying Video Analytic Services on the Edge

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains (VFC model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the VFC model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the VFC model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that VFC can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated

Enabling Real-Time AI Edge Video Analytics

This paper introduces a novel distributed AI model for managing in real-time, edge based intelligent analytics, such as the ones required for smart video surveillance. The novelty relies on distributing the applications in several decomposed functions which are linked together, creating virtual chain func- tions, where both computational and communication limitations are considered. Both theoretical analysis and simulation analysis in a real-case scenario have shown that the proposed model can enable real-time surveillance analytics on a low-cost edge network. Finally, a caching mechanism is proposed and evaluated, reducing further the operational costs of the edge network

Realising URRLC for Smart Energy Network Services

The growing introduction of DERs (Distributed Energy Resources) to the energy net-work translates to increased system stochasticity leading to the requirement of introduc-ing new Demand Response schemes with faster response times (low latency) and fast ancillary services, where flexible assets at the edge of the energy grid are used to support network stability. Multi-Access Edge Computing (MEC) is one of the 6G enabling tech-nologies proposed to meet the URRLC. Facilitating automation in the edge plays an important role in ensuring the smooth delivery of time-critical applications such as smart energy network services. This can be realised by orchestrating tasks by taking into account computing and communication dynamics, supporting live migration of Virtual Network Functions to maintain QoS and preventing deadlocks. This talk will present the use of MEC to dynamically map sensory information processing tasks near the physical information source allowing the realization of distributed smart energy services, like distributed Fast Frequency Response to be implemented

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

On the benchmarking of ResNet forgery image model using different datasets

This paper presents the benchmarking and improve- ment of the ResNet image forgery model using three different datasets (CASIA, Columbia, and LSBU). The model is based on classification, where forgery images have been edited using cut-paste modification technique.The images are categorized to check if the algorithm can successfully identify the difference between the original and the forgery image. All images have been pre-processed with Gray-Edge detectors to obtain get better classification results. Experimental results have shown that the Gray-edge technique has improved the accuracy across all image datasets

A New Forgery Image Dataset and its Subjective Evaluation

The aim of this research paper is to present a new forgery image dataset with a thorough subjective evaluation in detecting manipulated images, considering various parameters. The original images were obtained from public sources, and meaningful forgeries were produced using an image editing plat- form with three techniques: cut-paste, copy-move, and erase-fill. Both pre-processing and post-processing methods were used to generate fake images. The subjective evaluation revealed that the accuracy of manipulated image detection was affected by various factors, such as user type, image quantity, tampering method, and image resolution, which were analyzed using quantitative data

Federated Learning: Crop Classification in a Smart Farm Decentralised Network

In this paper, the application of federated learning to smart farming has been investi- gated. The Federated averaging model has been used to carry out crop classification using climatic parameters as independent variables and crop types as labels. The de- centralised machine learning models have been used to predict chickpea crops. Through experimentation, it has been observed the model converges when learning rates of 0.001 and 0.01 are considered using the Stochastic gradient descent (SGD) and the Adam optimizers. The model using the Adam optimizer converged faster than the SGD op- timizer, this was achieved after 100 epochs. Analysis from the farm dataset has shown that the decentralised models achieve faster convergence and higher accuracy than the centralised network models

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organized, self- managed and self-monitoring applications with ability to analyze and predict both the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for IoT applications in industry 5.0. Knowledge- Defined Networks (KDN) bridges this gap by extending SDN architecture with Knowledge Plane (KP) which learns the net- work dynamics to avoid sub-optimal decisions. Cognitive Routing leverages the Sixth-Generation (6G) Self-Organised-Networks with self-learning feature. This paper presents a self-organized cognitive routing frame- work for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most-reliable path with minimal probability of route-flapping. The proposed framework pre-calculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry stranded Link- state and distance-vector routing algorithms SPF and DUAL respectively

Evolution of Orchestration Towards 5G

Service orchestration is an essential activity in 5G networks. It performs optimal resource allocation and provisions services in an effective sequence based on demands across a collection of physical or virtual network functions (P/VNF). This paper summarizes several orchestration environments and components along with their evolution towards 5G. A brief operational comparison of platforms such as Open Source Management and Orchestration (OSM MANO), Open Platform for NFV (OPNFV) and Open Network Automation Platform (ONAP) have been presented, along with different deployment models and architectural alternatives