Fuzzy Reinforcement Learning Model for Resource Adaptation in IoT

The automation of several applications is creating engrossment in Internet of Things (IoT). The prerequisite for employing IoT in daily life is the ability to interact with devices technologies and process the sensed data. The difficulty to process the sensed data with scarce resources for diverse requests is challenging. Learning the behaviour of changing demand and processing with available resources has resulted in adaptation policy. Adaptation policy by employing Reinforcement learning and Fuzzy logic is proposed to adapt the resources for executing the tasks. Prioritization of task, allocating of resources to the tasks by adapting with available resources with assured Quality of Service (QoS) is performed. Fuzzy Q learning Adaptation Algorithm (FQAA) is designed for evaluating resource adaptation mechanisms to execute the heterogenous tasks. The algorithm with different configuration is simulated using Ifogsim and python. It is compared with traditional method that is without adaptation mechanism, performs better compared to other algorithms in terms of Cost, energy consumption and latency.

German–Norwegian relations in security and defence: What kind of partnership?

The article contributes to the debate on European defence cooperation and integration by analysing the German–Norwegian security and defence partnership. I define this partnership as being based upon mutual interests and values, and one in which the minor partner has important resources to contribute which accord with the larger partner’s interests and values. The article analyses this partnership within the framework of European integration and Atlantic cooperation, and in terms of how the discourse on strategic autonomy shapes the partnership. The article discusses two specific areas of cooperation, the High North and German–Norwegian collaboration on defence procurement. When analysing this relationship, I argue that Norway applies strategies such as acting as an external resource, adaptation and shielding to influence German policies vital to Norwegian security.

Design Ideas for an Issue-Situation-Based Board Game Involving Multirole Scenarios

In learning based on socio-scientific issues, teachers primarily use role playing in situation learning to promote students’ knowledge and responsibility and to teach them how to reach a consensus in multirole scenarios. However, if participants only engage with the material from the perspective of having one role, they will ignore comprehensive information on said issue. Therefore, the roles students play during multirole situational learning lead to diversity biases in learning results. To help participants enhance their competencies related to issues and eliminate learning gaps, we developed an issue-based board game named Water Ark based on the following four design ideas: multirole simulation, reflective goal, systemic situation, and interactions of society. The results indicated that after playing the game, participants’ knowledge and responsibility regarding water resource adaptation improved significantly. Furthermore, the findings revealed that the gap between the learning outcomes of participants with different roles in the game was not statistically significant, except in terms of students’ personal willingness to act. Thus, these design ideas could improve the utility of board games for learning about socio-scientific issues with multirole scenarios.