In order to get better results during brainstorming activities participants must respect some rules when they write notes about ideas and when they consider notes written by somebody else. We argue that the respect of these rules can be verified by a multi-agent system analyzing videos and notes produced by the participants in real time. This system can simplify the role of the meeting facilitator. Feedback is sent individually or is addressed globally to the entire team. This paper presents considerations about the necessity of rules, the structure of a multi-agent system for analyzing the respect of those rules and how experiments could show the level of acceptance by people of such a system. The goal of this prospective research is to add a non intrusive system during brainstorming sessions in order to enhance the quality of results.
<p>This paper investigates the long-term average age of information (AoI)-minimal problem in an unmanned aerial vehicle (UAV)-assisted wireless-powered communication network (WPCN), which consists of a static hybrid access point (HAP), a mobile UAV, and many static sensor nodes (SNs) randomly distributed on multiple islands. The UAV first is fully charged by the HAP, and then flies to each island to charge SNs and receive data from them. Before running out the energy in battery, the UAV flies back to the HAP to offload the received data and be fully charged again. Due to the finite battery capacity of the UAV, it is impossible for the UAV to traverse all the islands to collect all the data from SNs for once flight. We are thus inspired to divide islands into multiple clusters so that the UAV could traverse all the islands in each cluster. The key factors affecting the long-term average AoI contain the hovering duration, the flying duration, and the amount of data from each island reflected by the number of SNs on each island. Therefore, we formulate the long-term average AoI-minimal problem by jointly optimizing the transmit power of SNs, clustering of islands, and UAV's flight trajectory, subject to the battery capacity of the UAV. Since the optimization problem is NP-hard, there are no standard methods to solve it optimally in general. To tackle this problem, we decouple it into two subproblems: the power allocation subproblem for SNs, and the joint clustering of islands and UAV's flight trajectory design subproblem, which is much more perplexed and complicated owing to the tight coupling between them. To solve the first subproblem, we propose a hybrid TDMA and NOMA (HTN) protocol that takes advantage of the two protocols. To solve the second subproblem, we propose a clustering-based dynamic adjustment of the shortest path (C-DASP) algorithm, which is composed of three sub-algorithms, i.e, a proposed merging-aided K-means clustering (MaKMC) algorithm, the particle swarm optimization (PSO) algorithm employed to find the shortest path in each cluster, and a proposed dynamic adjustment (DA) algorithm taking into account the number of SNs on each island. Simulations are conducted to verify the effectiveness and superiority of the proposed HTN protocol and C-DASP algorithm.</p>
The use of the cloud computing system is expanding. As the number of terminals (things) connected to the cloud system increases, the limit of the capability is also becoming apparent. It leads to significant processing time delay. Edge (or fog) computing system is known as a method for improving a conventional cloud system. The basic idea is to consider a system that places edges (servers) between the cloud and the terminals (things). The capacity of each edge may not be so high, but many edges cooperate to execute tasks to achieve high processing power. Then, how should machine learning be realized on the edge system? Fast and secure learning methods are desired for machine learning. The use of a cryptographic system does not seem to be necessarily suitable for machine learning. Therefore, a safe system using distributed processing has attracted attention. SMC (Secure Multiparty Computation) is one of the typical models of them. Horizontally and vertically partitioned data are known for SMC. There have been proposed some methods for realizing machine learning on the cloud using SMC. Also, some methods of machine learning using horizontally partitioned data of SMC on the edge system have been proposed. On the other hand, little studies have been done on machine learning using the vertically partitioned data. In this paper, a fast and secure BP (Back-Propagation) neural network learning on vertically partitioned data with edge system is proposed. The effectiveness is shown by numerical simulation.
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