• Energy Research

This paper presents a novel algorithm for solving the cell assignment problem with special emphasis on energy awareness. The algorithm aims at finding the minimum number of base stations (BSs) that have to be turned on to guarantee the required service to the maximum number of users at lowest cost. The main contribution of the algorithm is the design of an effective solution that ensures an optimal assignment in a subset of base stations N ¿ N resulting in a drastic reduction of the search space within every subset N, eliminating the exponential growth over the number of users M, i.e. reducing the complexity from O(NM) to O(1). A branch-and-bound approach has been designed to determine the optimal base station assignments. Experiments demonstrate that our solution performs as expected in terms of profit, served clients, and energy savings due to active BSs, at the expense of very reasonable execution time overhead. Peer Reviewed

This dataset contains the measuments registered by the programmable logical controller (PLC) of a residential hydrogen-based energy system. The system corresponds to a pilot plant located in Novales (Spain) based on the hybridization of renewable energy system and hydrogen-based technologies to accomplish the 100% electrical self-sufficiency of a dwelling. This pilot plant works as follows: Photovoltaic panels harvest the solar energy that is supplied to the home through a hybrid inverter. The existing surpluses are firstly stored in a battery pack, that saves energy in a day-to-day basis. Subsequently, in case of further surpluses, the electricity is employed to store energy in the form of hydrogen, which is generated by an electrolyzer, yielding seasonal energy accumulation. This hydrogen is sent to a buffer tank. When the buffer reaches its maximum pressure, a compressor is triggered to accumulate the generated hydrogen in a high-pressure tank. Finally, if additional electricity excesses exist, these are injected back to the grid, and sold to the utility grid company to obtain additional revenues. If there is not sufficient PV generation to meet the demand of the dwelling, the batteries cover the electricity deficit in a first stage. If the energy stored in the battery pack is not enough to ensure the continuous electrical supply, a fuel cell is triggered, either with the hydrogen stored in the buffer tank or in the high-pressure tank. The fuel cell can also charge the battery pack apart from covering the electricity required by the home. The decision-making to switch on/off the electrolyzer, compressor or fuel cell is carried out by a PLC based on the comparison of the system variables with a predefined set of thresholds, programmed into the PLC, as shown in the next figure. The measurements were collected every 5 seconds during the time period of March-April 2024. The.csv file format is following described. Each row represents a sample from the real system, and the colums the measuments colleted: datetime (datetime): Timestamp of the measurements, in "yyyy-MM-dd hh:mm:ss" format. Breaks (string): Indicates the starting ("OPEN") and end ("CLOSE") of a period of measurements. Intermediate samples should contain empty values. P_pan (float): Power generated by the photovoltaic panels [Watts]. P_viv (float): Power consumed in the dwelling[Watts]. P_car (float): Changing battery power [Watts]. P_des (float): Discharching battery power [Watts]. P_exp (float): Power exported to the grid [Watts]. P_imp (float): Power imported from the grid [Watts]. P_elec (float): Power consumed by the electrolizer [Watts] P_comp (float): Power consumed by the compressor [Watts]. P_pur (float): Power consumed by purifier [Watts]. SOC (float): state of charge of the battery [%]. pres_buf (float): Buffer pressure [Bar]. pres_comp (float): Tank pressure [Bar]. P_pila (float): Power provided by the fuel cell [Watt]. pres_pila (float): Fuel Cell pressure [Bar]. h2_flow_pila (float): Hydrogen flow consumed by the fuel cell [NL/h]. h2_total (float): Total hydrogen generated by the electrolyzer [NL]. h2_flow (float): Hydrogen flow generatedby the electrolyzer in the pariod between current sample and the one before [NL/h] Temperature (float): Environmental temperature [ºC].

Le problème d'affectation des cellules est combinatoire, avec une complexité accrue lorsqu'il est abordé compte tenu de l'allocation des ressources. Ce document modélise l'affectation conjointe des cellules et l'affectation des ressources pour les réseaux hétérogènes cellulaires, et formalise l'affectation des cellules en tant que problème d'optimisation. Les algorithmes exacts peuvent trouver des solutions optimales au problème d'affectation des cellules, mais leur temps d'exécution augmente considérablement avec des déploiements de réseau réalistes. À leur tour, les heuristiques sont capables de trouver des solutions dans des délais d'exécution raisonnables, mais elles sont généralement bloquées dans des optima locaux, ne trouvant donc pas de solutions optimales. Les approches métaheuristiques ont réussi à trouver des solutions plus proches de l'optimum que les problèmes combinatoires pour les grands cas. Dans cet article, nous proposons une heuristique rapide et efficace qui donne des solutions d'affectation de cellules très compétitives par rapport à celles obtenues avec trois des métaheuristiques les plus largement utilisées, qui sont connues pour trouver des solutions proches de l'optimum en raison de la nature de leur exploration de l'espace de recherche. Notre approche heuristique ajoute une réduction des dépenses énergétiques dans sa conception algorithmique. Grâce à la simulation et à l'analyse statistique formelle, le système proposé s'est avéré produire des affectations efficaces en termes de nombre d'utilisateurs desservis, d'allocation de ressources et d'économies d'énergie, tout en étant un ordre de grandeur plus rapide que les approches métaheuritsiques. El problema de asignación de celdas es combinatorio, con una mayor complejidad cuando se aborda teniendo en cuenta la asignación de recursos. Este documento modela la asignación conjunta de celdas y la asignación de recursos para redes celulares heterogéneas, y formaliza la asignación de celdas como un problema de optimización. Los algoritmos exactos pueden encontrar soluciones óptimas al problema de asignación de celdas, pero su tiempo de ejecución aumenta drásticamente con implementaciones de red realistas. A su vez, las heurísticas son capaces de encontrar soluciones en tiempos de ejecución razonables, pero generalmente se quedan atascadas en los óptimos locales, por lo que no encuentran soluciones óptimas. Los enfoques metaheurísticos han tenido éxito en la búsqueda de soluciones más cercanas a la óptima para problemas combinatorios para grandes instancias. En este artículo proponemos una heurística rápida y eficiente que produce soluciones de asignación de celdas muy competitivas en comparación con las obtenidas con tres de las metaheurísticas más utilizadas, que se sabe que encuentran soluciones cercanas a las óptimas debido a la naturaleza de su exploración del espacio de búsqueda. Nuestro enfoque heurístico añade una reducción del gasto energético en su diseño algorítmico. A través de la simulación y el análisis estadístico formal, se ha demostrado que el esquema propuesto produce asignaciones eficientes en términos del número de usuarios atendidos, la asignación de recursos y el ahorro de energía, a la vez que es un orden de magnitud más rápido que los enfoques basados en metaheurística. The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches. مشكلة تخصيص الخلايا هي مشكلة اندماجية، مع زيادة التعقيد عند معالجتها مع مراعاة تخصيص الموارد. تقوم هذه الورقة بنمذجة تخصيص الخلايا المشتركة وتخصيص الموارد للشبكات الخلوية غير المتجانسة، وإضفاء الطابع الرسمي على تخصيص الخلايا كمشكلة تحسين. يمكن للخوارزميات الدقيقة العثور على الحلول المثلى لمشكلة تعيين الخلية، ولكن وقت تنفيذها يزداد بشكل كبير مع عمليات نشر الشبكة الواقعية. في المقابل، يستطيع الاستدلال إيجاد حلول في أوقات تنفيذ معقولة، لكنهم عادة ما يعلقون في الوضع الأمثل المحلي، وبالتالي يفشلون في إيجاد الحلول المثلى. نجحت الأساليب الماورائية في إيجاد حلول أقرب إلى الحل الأمثل للمشاكل التوافقية في الحالات الكبيرة. نقترح في هذه الورقة الاستكشافية السريعة والفعالة التي تسفر عن حلول تعيين خلية تنافسية للغاية مقارنة بتلك التي تم الحصول عليها مع ثلاثة من metaheuristics الأكثر استخدامًا، والتي من المعروف أنها تجد حلولًا قريبة من الأمثل نظرًا لطبيعة استكشافهم لفضاء البحث. يضيف نهجنا الاستكشافي تخفيضًا في إنفاق الطاقة في تصميمه الخوارزمي. من خلال المحاكاة والتحليل الإحصائي الرسمي، ثبت أن المخطط المقترح ينتج مهامًا فعالة من حيث عدد المستخدمين الذين يتم خدمتهم وتخصيص الموارد وتوفير الطاقة، بينما يكون ترتيبًا من حيث الحجم أسرع من النهج القائمة على metaheuritsic.

The QUIC protocol, which was originally proposed by Google, has recently gained a remarkable presence. Although it has been shown to outperform TCP over a wide range of scenarios, there exist some doubts on whether it might be an appropriate transport protocol for IoT. In this paper, we specifically tackle this question, by means of an evaluation carried out over a real platform. In particular, we conduct a thorough characterization of the performance of the MQTT protocol, when used over TCP and QUIC. We deploy a real testbed, using commercial off-the-shelf devices, and we analyze two of the most important key performance indicators for IoT: delay and energy consumption. The results evince that QUIC does not only yield a notable decrease in the delay and its variability, over various wireless technologies and channel conditions, but it does not hinder the energy consumption.