The introduction of several new unlicensed spectrum bands in Beyond 5G (B5G) and HetNet enables unprecedented data rates, ultra-low latency, and the capacity to support vast numbers of connected devices. However, to fully realize the potential of unlicensed spectrum bands, efficient management is essential. Traditionally, managing unlicensed bands has focused on controlling contention and interference, often using protocols like LTE-U and Listen Before Talk (LBT). Yet, because unlicensed spectrum is shared, achieving fair usage across users remains a challenge. In this paper, we present a decentralized approach for managing unlicensed spectrum in B5G HetNets. We propose a population-based spectrum sharing with soft-threshold variable to reduce hoarding and to improve fairness. The resource sharing logic is implemented through smart contract to facilitate fair and efficient spectrum sharing among coexisting base stations. Simulations comparing different methods, including fairness-enabled and inter-cell LBT approaches, reveal that our solution reduces both spectrum hoarding and rejection rates compared to solutions that do not use fair-sharing.

360° images offer panoramic views of captured environments, placing users within an egocentric perspective. While users can freely rotate their viewpoint, they don’t experience 6-DoF navigation with translational movement. In this research, we introduce Avatar360, a novel method to elicit 6-DoF perception in 360° panoramas, using avatar-assisted navigation combined with an exocentric view of the 360° panorama. We seamlessly integrate a 3D avatar into 360° panoramas, allowing users to navigate a 3D virtual landscape congruent with the 360° background. By aligning the exocentric perspective of the 360° panorama with the avatar’s movements, we replicate a sensation of 6-DoF navigation in 360° panoramas. We explore mechanisms for simultaneous avatar and viewpoint controls, as well as procedures for transitions between spatially connected 360° panoramas. A user study was conducted to assess the perception of 6-DoF navigation in 360° panoramas via a 3D avatar, evaluating users’ sense of movement, disorientation, and presence. We also gained insight into perspective view controls and transition techniques between panoramas. Statistical analysis shows avatar-assisted navigation elicits a user’s sense of movement within 360° panoramas. Our results also provide guidelines for effective view control and transition strategies in avatar-assisted 360° navigation.

We present MRMAC, a Mixed Reality Multi-user Asymmetric Collaboration system that allows remote users to teleport virtually into a real-world collaboration space to communicate and collaborate with local users. Our system enables telepresence for remote users by live-streaming the physical environment of local users using a 360° camera while blending 3D virtual assets into the mixed-reality collaboration space. Our novel client-server architecture enables asymmetric collaboration for multiple AR and VR users and incorporates avatars, view controls, as well as synchronized low-latency audio, video, and asset streaming. We evaluated our implementation with two baseline conditions: conventional 2D and standard 360° videoconferencing. Results show that MRMAC outperformed both baselines in inducing a sense of presence, improving task performance, usability, and overall user preference, demonstrating its potential for immersive multi-user telecollaboration.

Mixed-perspective, combining egocentric (first-person) and exocentric (third-person) viewpoints, have been shown to improve the collaborative experience in remote settings. Such experiences allow remote users to switch between different viewpoints to gain alternative perspectives of the remote space. However, existing systems lack seamless selection and transition between multiple perspectives that better fit the task at hand. To address this, we present a new approach called Vicarious, which simplifies and automates the selection between egocentric and exocentric viewpoints. Vicarious employs a context-aware method for dynamically switching or highlighting the optimal viewpoint based on user actions and the current context. To evaluate the effectiveness of the viewpoint selection method, we conducted a user study (n = 27) using an asymmetric AR-VR setup where users performed remote collaboration tasks under four distinct conditions: No-view, Manual, Guided, and Automatic selection. The results showed that Guided and Automatic viewpoint selection improved users’ understanding of the task space and task performance, and reduced cognitive load compared to Manual or No-view selection. The results also suggest that the asymmetric setup had minimal impact on spatial and social presence, except for differences in task load and preference. Based on these findings, we provide design implications for future research in mixed reality collaboration.

Ensuring the accuracy and completeness of drug labels is a labour-intensive and potentially error prone process, as labels contain unstructured text that is not suitable for automated processing. To address this, we have developed a novel deep learning system that uses a bidirectional LSTM model to extract and structure drug information in a knowledge graph-based embedding space. This allows us to evaluate drug label consistency with ground truth knowledge, along with the ability to predict additional drug interactions. Annotated sentences from 7,117 drug labels sentences were used to train the LSTM model and 1,779 were used to test it. The drug entity extraction system was able to correctly detect relevant entities and relations with a F1 score of 91% and 81% respectively. The knowledge graph embedding model was able to identify inconsistent facts with ground truth data in 76% of the cases tested. This demonstrates that there is potential in building a natural language processing system that automatically extracts drug interaction information from drug labels and embeds this structured data into a knowledge graph embedding space to help evaluate drug label accuracy. We note that the accuracy of the system needs to be improved significantly before it can fully automate drug labeling related tasks. Rather such a system could provide best utility within a human-in-the-loop approach, where operators augment model training and evaluation.

Connected healthcare is the process of integrating healthcare smart applications into smart devices. These systems can enable better patient-hospital experience, efficient time usage, reduced errors, safety and security, and ultimately improved treatments. These smart devices which form an IoT network are extremely dynamic because of the user movement. In an environment where there is a constant change in the network topology and its traffic profile, it is a challenging task to provide reliable network connectivity and to maintain the IoT network. Therefore, ensuring healthcare traffic is resilient towards change in the traffic profile is of paramount importance. This paper leverages graph theory concepts to understand the behaviour of the healthcare IoT network. The paper highlights the importance of the PN (PN) and traffic splitting (stratification). A PN is a node in the network which has enough computing resources to share with other network devices. By optimizing the selection of PN, the drastic improvement in the network performance could be achieved. Moreover, we show that splitting the traffic along with optimized PN selection minimizes the chances of healthcare traffic drop during the period of high network usage.

Mobile Edge Computing (MEC) and Network Slicing techniques have a potential to augment 5G-IoT network services. Telecommunication operators use a diverse set of radio access technologies to provide services for users. Mobility management is one such service that needs attention for new 5G deployments. The QoS requirements in 5G networks are user specific. Network slicing along with MEC has been promoted as a key enabler for such on-demand service schemes. This paper focuses on radio resource access across heterogeneous networks for mobile roaming users. A unified service architecture is proposed enabling seamless handover between a 5G (New Generation Core) service and a 4G (Evolved Packet Core) service via the network slicing paradigm. An identifier-locator (I-L) concept that allows active source-IP sessions is used to handle the seamless hand-over. Signaling costs, service disruptions and other resource reservation requirements are considered in the evaluation to assure that profit for mobile edge operators is achieved. Simulation experiments are considered to provide performance comparisons against the state-of-the-art Distributed Mobility Management Protocol (DMM).

With the technological evolution in telecommunication networks, performance requirements such as better coverage, higher bandwidth, and lower latency have been pushed to new horizons. However, as a direct result network complexity has increased dramatically over the recent years, and with this complexity manageability has suffered. This paper presents the architecture of the E-Stream project which aims to support Next Generation Operations Support Systems. E-Stream applies dimension reduction, data mining, and recommender system techniques in order to handle very high volumes of management events, identify and predict network incidents, and recommend candidate corrective actions to domain experts in Network Operations Centres.

Currently the volume of telecom network management data is expanding exponentially, mainly due to the explosive growth in the number of communicating devices along with the increase in heterogeneity of the networks. Such scale of data obsoletes the traditional approach of extracting offline analytics from the network traces governed by some pre-defined schemes. In order to increase the efficiency of the Operations Support System (OSS) and gain in-depth understanding of the generic relationship between network entities, the monitoring data needs to undergo large-scale deep analytics processing. In this paper we present i-MagNet, an integrated analytics framework developed with the popular real-time stream processing paradigm Storm. The components of i-MagNet intelligently micro-batch segments of incoming streams to enable high-throughput online analytics of management trace streams. Inter-dependence metrics (temporal and statistical) are exploited to extract contiguous event subsequences, which can then be independently examined as part of a network incident analysis system.

Monitoring and management of modern telecommunication networks has become more and more challenging due to the explosion in scale of data generated by network elements. Not only has the size of the network, the number of nodes, and the number of customers increased, but the amount and dimensionality of the data coming from each managed element has also increased. To support sophisticated monitoring and management strategies it is desirable to forward as much trace data as possible into operators' operations support systems (Operations Support Systems (OSSs)). In this paper a heuristic algorithm is presented which reduces the data-stream by removing uncorrelated noise events by determining the degree of inter-relationship between the events in the data-stream. With a sophisticated open source control plane emulator used as the source generator, the results show that the presented algorithm is capable of differentiating noise from useful information thus significantly reducing scale and dimensionality of network monitoring data-streams.

This paper presents a novel ensemble model of artificial neural networks for rainfall forecast incorporating dynamic variable selection. In the first phase of the model, meteorological variables optimal to the response (here rainfall) are selected with the optimal lag value of the response variable. A dynamic variable selection method named, time series least angle regression (TS-LARS) is applied in this phase. In the second phase, an ensemble comprising artificial neural network (ANN) is constructed. The number of hidden neurons in each ANN are selected randomly to speed up the training of the ensemble. The optimization of each ANN is done by Levenberg Marquart Gradient Descent method. In the third phase of the ensemble, the component ANN models are ranked based on mutual information (MI) between the outputs of the base models and the original output. Before applying MI, we have used independent component analysis (ICA) to extract the base models which are independent with each other. Finally the highest ranked base models are combined to construct the ensemble model. A real world case study has been setup in Fukuoka city, Japan. Daily rainfall data from 1990 to 2010 with relevant meteorological variables are extracted to construct the data. The empirical results reveal that, the use of TS-LARS to select most relevant dynamic variables increase the efficiency of the ensemble model, where as the ICA-MI method reduce the number of base models hence reduce the complexity of the ensemble.