Contribution to the Management of Energy in the Systems Multi Renewable Sources with Energy by the Application of the Multi Agents Systems “MAS”

AbstractGiven the current energy challenge, renewable energy appear as a real and strategic solution for electricity generation, but the intermittent nature this type of energy we forced to combine at least two power sources to ensure continuity in supply of electricity.Typically multi-source renewable energy systems are managed by centralized approaches, but the limit of these approaches in several aspects such as the dynamic aspect management of system, integration or cancellation of one or more elements we require seek other more reliable approaches for the management of multi-source renewable energy systems.The proposed solution is an integration of Multi Agent Systems “MAS” in energy management, this discipline is the connection of several fields such as artificial intelligence, distributed computing systems and software engineering. “MAS” it is discipline that focuses on collective behaviors produced by the interactions of several autonomous entities called agents, these interactions revolve around cooperation, competition or coexistence between these agents, introducing the issue of collective intelligence and the emergence of structures interactions

A self-integration testbed for decentralized socio-technical systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities

Distributed intelligence for pervasive optical network telemetry

Optical network automation and failure management require measuring the status and the performance of the different network devices to anticipate any degradation and ensure the quality of the provided services, i.e., optical connectivity. Such pervasive network telemetry entails collecting large amounts of measurements and events from different sources and with very fine granularity, which given the amount and variety of telemetry sources and the size of each measurement and event, imposes requirements that are hard to achieve without large investments. In this paper, we analyze the main limitations of telemetry architectures relying exclusively on centralized systems for data analysis and propose an architecture with distributed intelligence. Data aggregation techniques, especially conceived for optical network telemetry, are presented with the objective of reducing data dimensionality. Illustrative results from our experimental telemetry system reveal a reduction of 3 orders of magnitude in terms of total data volume without introducing significant error and processing delay and, more importantly, helping network automation algorithms to identify meaningful changes in the network status.HORIZON EUROPE Framework Programme [SEASON (101096120)]; Agencia Estatal de Investigación [IBON (PID2020-114135RB-I00)]; Institució Catalana de Recerca i Estudis Avançats.Peer ReviewedPostprint (author's final draft

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy- Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

An Architecture for Integrated Intelligence in Urban Management using Cloud Computing

With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates the application of cloud capacity to support the information, communication and decision making needs of a wide variety of stakeholders in the complex business of the management of urban and regional development. The complexity lies in the interactions and impacts embodied in the concept of the urban-ecosystem at various governance levels. This highlights the need for more effective integrated environmental management systems. This paper offers a user-orientated approach based on requirements for an effective management of the urban-ecosystem and the potential contributions that can be supported by the cloud computing community. Furthermore, the commonality of the influence of the drivers of change at the urban level offers the opportunity for the cloud computing community to develop generic solutions that can serve the needs of hundreds of cities from Europe and indeed globally.Comment: 6 pages, 3 figure