Search

Your search keyword '"Lukkien, Johan"' showing total 147 results
Start Over Author "Lukkien, Johan" Publisher ieee
147 results on '"Lukkien, Johan"'

1. Obtaining insights into the interplay between systems and software engineering

Author

Vasenev, Alexandr, Lukkien, Johan, van Veen, Laura, Goosen, Pieter, Doornbos, Richard, Mooij, Arjan, Vasenev, Alexandr, Lukkien, Johan, van Veen, Laura, Goosen, Pieter, Doornbos, Richard, and Mooij, Arjan

Abstract

The high-tech equipment industry is adopting approaches like Model-Based Systems Engineering (MBSE) and Scaled Agile Framework (SAFe) to improve their practices. Instantiations of these approaches do not necessarily align across disciplines. Given the importance of software aspects in systems, the interplay between software and other engineering disciplines should be carefully addressed. Decision makers are interested in mitigating any risks in the collaboration of specialists in different disciplines. This short positioning paper proposes an applied research approach to identify such risks for later mitigation. The approach is inspired by grounded theory (we constructed concepts from interviews) and systems thinking. Work still to do is to cross-check identified concepts with literature. This research is in progress and we welcome discussions and critical opinions.

Published
2024

7. Deep reinforcement learning for IoT network dynamic clustering in edge computing

Author

Liu, Qingzhi, Cheng, Long, Ozcelebi, Tanir, Murphy, John, and Lukkien, Johan

Subjects
Artificial intelligence, Big Data, data analysis, Internet of Things, learning, pattern clustering, Deep Reinforcement Learning, DQN, IoT Network, Dynamic Clustering, Edge Computing
Abstract

How to process the big data generated in large IoT networks is still challenging current techniques. To date, a lot of network clustering approaches have been proposed to improve the performance of data aggregation in IoT. However, most of them focus on partitioning networks with static topologies, and thus they are not optimal on handling the case with moving objects in the networks. Moreover, as the best of knowledge, none of them has ever considered the performance of following computing in edge servers. To improve these problems, in this work, we propose a highly efficient IoT network dynamic clustering solution in edge computing using deep reinforcement learning (DRL). Our approach can both fulfill the data communication requirements from IoT networks and load-balancing requirements from edge servers, and thus provide a great opportunity for future high performance IoT data analytics. We implement our approach by Deep Q-learning Network (DQN) model, and our preliminary experimental results show that the DQN solution can achieve higher score in cluster partitioning compared with the current static benchmark solution.

Published
2019

8. CluFlow: Cluster-based Flow Management in Software-Defined Wireless Sensor Networks

Author

Liu, Qingzhi (author), Ozcelebi, Tanir (author), Cheng, Long (author), Kuipers, F.A. (author), Lukkien, Johan (author), Liu, Qingzhi (author), Ozcelebi, Tanir (author), Cheng, Long (author), Kuipers, F.A. (author), and Lukkien, Johan (author)

Abstract

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks, for instance in terms of reduced management complexity and more fine-grained traffic engineering. However, the design and implementation of SDN within wireless sensor networks (WSN) have received far less attention. Unfortunately, because of the multi-hop type of communication in WSN, a direct reuse of the wired SDN architecture could lead to excessive commu- nication overhead. In this paper, we propose a cluster-based flow management approach that makes a trade-off between the granularity of monitoring by an SDN controller and the communication overhead of flow management. A network is partitioned into clusters with a minimum number of border nodes. Instead of having to handle the individual flows of all nodes, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes. Our proof-of- concept implementations in software and hardware show that, when compared with benchmark solutions, our approach is significantly more efficient with respect to the number of nodes that must be managed and the number of control messages exchanged., Embedded and Networked Systems

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results