The paper CluFlow: Cluster-based Flow Management in Software-Defined Wireless Sensor Networks was accepted by The 2019 IEEE Wireless Communications and Networking Conference. WCNC is a CORE-B conference, and it is also a world premier wireless event.

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 communication 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-ofconcept 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.

The paper Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment was accepted by IEEE Transactions on Services Computing. The whole review process takes about 21 months with two round revisions, TSC is a flagship journal in services computing.

Abstract: Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs. Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns hybrid process models to bridge this gap. Moreover, to cope with today’s big event logs, we propose an efficient method, called f-HMD, aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalable.