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In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.

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Urban mobility efficiency is of utmost importance in big cities. Taxi vehicles are key elements in daily traffic activity. The advance of ICT and geo-positioning systems has given rise to new opportunities for improving the efficiency of taxi fleets in terms of waiting times of passengers, cost and time for drivers, traffic density, CO2 emissions, etc., by using more informed, intelligent dispatching. Still, the explicit spatial and temporal components, as well as the scale and, in particular, the dynamicity of the problem of pairing passengers and taxis in big towns, render traditional approaches for solving standard assignment problem useless for this purpose, and call for intelligent approximation strategies based on domain-specific heuristics. Furthermore, taxi drivers are often autonomous actors and may not agree to participate in assignments that, though globally efficient, may not be sufficently beneficial for them individually.This paper presents a new heuristic algorithm for taxi assignment to customers that considers taxi reassignments if this may lead to globally better solutions. In addition, as such new assignments may reduce the expected revenues of individual drivers, we propose an economic compensation scheme to make individually rational drivers agree to proposed modifications in their assigned clients. We carried out a set of experiments, where several commonly used assignment strategies are compared to three different instantiations of our heuristic algorithm. The results indicate that our proposal has the potential to reduce customer waiting times in fleets of autonomous taxis, while being also beneficial from an economic point of view.

AbstractIn this article, we examine the state-of-the-art and current applications of artificial intelligence (AI), specifically for human resources (HR). We study whether, due to the experimental state of the algorithms used and the nature of training and test samples, a further control and auditing in the research community is necessary to guarantee fair and accurate results. In particular, we identify the positive and negative consequences of the usage of video-interview analysis via AI in recruiting processes as well as the main machine learning techniques used and their degrees of efficiency. We focus on some controversial characteristics that could lead to ethical and legal consequences for candidates, companies and states regarding discrimination in the job market (e.g. gender and race). There is a lack of regulation and a need for external and neutral auditing for the type of analyses done in interviews. We present a multi-agent architecture that aims at total legal compliance and more effective HR processes management.

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The authors present the development of an autonomous solar-powered wireless node for low-cost, static mesh networks. IEEE 802.11 is used for communication among nodes.