Disciplined Exploration of Emergence Using Multi-Agent Simulation Framework

In recent years the concept of emergence has gained much attention as ICT systems have started exhibiting properties usually associated with complex systems. Although emergence creates many problems for engineering complex ICT systems by introducing undesired behaviour, it also offers many possibilities for advance in the area of adaptive self-organizing systems. However, at the moment the inability to predict and control emergent phenomena prevents us from exploring its full potential or avoiding problems in existing complex systems. Towards this end, this paper proposes a framework for empirical study of complex systems exhibiting emergence. The framework relies on agent-oriented modelling and simulation as a tool for examination of specific manifestations of emergence. The main idea is to use an iterative simulation process in order to build a coarse taxonomy of causal relationships between the micro- and macro layers. In addition to the detailed description of the framework, the paper also discusses the corresponding verification and validation processes as important factor for the success of such a study

Towards 6G IoT : tracing mobile sensor nodes with deep learning clustering in UAV networks

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs

Public engagement practices in EC-funded RRI projects: Fostering socio-scientific collaborations

The "ambiguity" of Research and Innovation (R&I) within the present contemporary society triggers increasing manifestations of public concerns concerning science. Apart from some implications it has, this mistrust also functions as a stimuli towards integrating the public view and public (social) needs into the development and implementation of R&I policies. With reference to European communities, the European Commission (EC) has provided funding to various projects aiming to capitalise on the concept of Responsible Research and Innovation (RRI) and the RRI "key" of Public Engagement (PE) in order to engage the public in R&I, enhance a human-centric and inclusive R&I approach, and ultimately foster a mutually responsible relation between science and society. This study aims to examine how PE practices are implemented within the context of EC-funded projects addressing RRI-driven public engagement. Seventeen PE practices that have been implemented during the lifespan of five EC projects were qualitatively and thematically analysed. The identified themes indicate the implementation patterns of PE and contribute to reaching a set of conclusions towards realising a participatory, human-centric and inclusive R&I, fostering in its own turn future socio-scientific collaborations. Policy-makers, researchers, practitioners and stakeholders interested in public engagement in R&I can capitalise on the study's conclusions and contribute to manifestations of responsible innovation

Public Engagement Practices in EC-Funded RRI Projects: Fostering Socio-Scientific Collaborations

The ‘ambiguity’ of Research and Innovation (R&I) within the present contemporary society triggers increasing manifestations of public concerns concerning science. Apart from some implications it has, this mistrust also functions as a stimuli towards integrating the public view and public (social) needs into the development and implementation of R&I policies. With reference to European communities, the European Commission (EC) has provided funding to various projects aiming to capitalise on the concept of Responsible Research and Innovation (RRI) and the RRI ‘key’ of Public Engagement (PE) in order to engage the public in R&I, enhance a human-centric and inclusive R&I approach, and ultimately foster a mutually responsible relation between science and society. This study aims to examine how PE practices are implemented within the context of EC-funded projects addressing RRI-driven public engagement. Seventeen PE practices that have been implemented during the lifespan of five EC projects were qualitatively and thematically analysed. The identified themes indicate the implementation patterns of PE and contribute to reaching a set of conclusions towards realising a participatory, human-centric and inclusive R&I, fostering in its own turn future socio-scientific collaborations. Policy-makers, researchers, practitioners and stakeholders interested in public engagement in R&I can capitalise on the study’s conclusions and contribute to manifestations of responsible innovation