Artificial Intelligence of Things (AIoT) represents a technological revolution that integrates the processing of Artificial Intelligence (AI) with the Internet of Things (IoT). This work proposes an AIoT-based Mobile Edge Computing Architecture that enables the decentralization of the on-demand decision-making process in remote environments through hierarchical network management, emphasizing the edge and fog layers. The architecture aims to apply AI in an airborne IoT network infrastructure to increase the efficiency of applications composed of low-power computing devices in terms of processing, communication, and energy. The proposed architecture aims to direct the adequate use of AI models in a decentralized manner on-demand, seeking to improve applications in terms of latency, bandwidth consumption, and energy. The study showed that the NCNN format offers the best energy efficiency and processing speed at the edge layer, processing up to 219 frames in real-time with only 3.57 mAh and 80 ms per image. While fog computing reduced memory usage, it increased latency over 40 meters, highlighting the need for hybrid strategies.

This paper addresses the problem of BPM and SOA integration presenting a model of dynamic discovery of services as an approach to improve the agility of SOA-based applications. Many researches have tackled the discovery problem. However, in general they fail in discovering the most suitable service regarding business processes needs. The main contribution of this work is to offer a comprehensive, integrated, open and standard-based environment which considers functional and non-functional requirements besides business processes' contexts. The discovery acts over largely distributed service providers over the Internet (published in several UDDIs) making use of proper QoS and UBL ontologies and accessed under the SaaS model. An electronic catalog is also developed to assist the SOA designer to better capture business processes requirements and contexts.

This paper presents a framework for supporting virtual organization (VO) inheritance relying on knowledge management approach, It emphasizes that part of the problems to manage VOs is derived fiom the lack of information and lessons learned about the past businesses as well as that keeping the VOs’ histories can be useful for augmenting and for given more agility in the decision-making process. The suggested framework presents VO inheritance as a horizontal activity that covers the entire VO life cycle, providing information for the several involved collaborative actors. An initial experiment using decision analysis is presented to show its use in VO inheritance. In the conclusions it is argued that VOs could be much more effective and the problems solved with more agility if a better use could be made out of the information inherited from VOs.

This paper presents an approach to help VE managers in the selection of the most suitable enterprises to compose a VE (Virtual Enterprise). Applying the Analytic Hierarchy Process (AHP) method having as basis metrics presented in the SCOR model, the VE manager can have a more robust view upon the partners’ importance and bill of material’s items criticality in a given business opportunity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The management of Virtual Organization (VO) brings some challenges. One of them is the appropriate and effective coordination and monitoring of distributed businesses processes. It is not easy or trivial to handle the enormous amount of information about and from VOs members. As a direct consequence, decisions are crucial and should be taken not only with agility, but also with intelligence. This paper introduces a framework to support an integrated decision-making environment through which managers will be able to take assisted and smarter decisions. The key element of this framework is the Intelligent Decision Support System, which applies Artificial Intelligence techniques (Data Mining and Machine Learning) to facilitate and to support the decision-making process. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.