- Multi-Energy Management of Buildings in Smart Grids ( ), University of Groningen, 2021.
- Automated Service Composition Using AI Planning and Beyond ( ), Chapter in (M. Aiello, A. Bouguettaya, D. A. Tamburri, W. J. van den Heuvel, eds.), Springer International Publishing, 2021.
- Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks ( ), In Processes, volume 9, 2021.
By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.
- TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. ( ), In In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and its Applications., Elsevier, volume 122, 2021.
- Employability prediction: a survey of current approaches, research challenges and applications ( ), In Journal of Ambient Intelligence and Humanized Computing, 2021.
Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. Knowing their weaknesses and strengths, students might better plan their career. Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers. All these combined efforts certainly can contribute to increasing employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. More and more studies are investigating data mining techniques for the prediction of employability. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.
- OpenBNG: Central office network functions on programmable data plane hardware ( ), In International Journal of Network Management, Wiley, volume 31, 2021.
- SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks ( ), In BMC bioinformatics, BioMed Central, volume 22, 2021.
- Adaptive On-the-fly Changes in Distributed Processing Pipelines ( ), In Frontiers in Big Data, Frontiers, 2021.
- A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue ( ), In International Journal of Advanced Robotic Systems, volume 18, 2021.
The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.
- Digital Twins: an enabler for digital transformation ( ), Chapter in The Digital Transformation handbook, 2021.
- Accelerating the Performance of Data Analytics using Network-centric Processing ( ), In The 15th ACM International Conference on Distributed and Event-based Systems (DEBS '21), June 28-July 2, 2021, Virtual Event, Italy, ACM New York, NY, USA, 2021.
- Leveraging Flexibility of Time-Sensitive Networks for dynamic Reconfigurability ( ), In Proceedings of IFIP Networking 2021, IFIP, 2021.
- Leveraging PIFO Queues for Scheduling in Time-Sensitive Networks ( ), In In the Proceedings of the IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN 2021)., IEEE, 2021.
- P4-CoDel: Experiences on Programmable Data Plane Hardware ( ), In Proceedings of the IEEE International Conference on Communications (ICC 2021): Next-Generation Networking and Internet Symposium, IEEE, 2021.
- Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming ( ), In Proceedings of the Conference on Networked Systems 2021 (NetSys 2021), European Association of Software Science and Technology, 2021.
- Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications ( ), volume 13, 2021.
Safe water is becoming a scarce resource, due to the combined effects of increased population, pollution, and climate changes. Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback. Recently developed systems utilizing wireless sensor network (WSN) technology have reported weaknesses in energy management, data security, and communication coverage. Due to the recent advances in Internet-of-Things (IoT) that can be applied in the development of more efficient, secure, and cheaper systems with real-time capabilities, we present here a survey aimed at summarizing the current state of the art regarding IoT based smart water quality monitoring systems (IoT-WQMS) especially dedicated for domestic applications. In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS via a proposed empirical metric, analysis, and discussion and, finally, design recommendations for an efficient system. No doubt, this study will benefit the developing field of smart homes, offices, and cities.