Research Publications

TS-LoRa: Time-slotted LoRaWAN for the Industrial Internet of Things

TS-LoRa: Time-slotted LoRaWAN for the Industrial Internet of Things Zorbas, Dimitrios; Abdelfadeel, Khaled; Kotzanikolaou, Panayiotis; Pesch, Dirk Automation and data capture in manufacturing, known as Industry 4.0, requires the deployment of a large number of wireless sensor devices in industrial environments. These devices have to be connected via a reliable, low-latency, low-power and low operating-cost network. Although LoRaWAN provides a low-power and reasonable-cost network technology, its current ALOHA-based MAC protocol limits its scalability and reliability. A common practise in wireless networks is to solve this issue and improve scalability through the use of time-slotted communications. However, any time-slotted approach comes with overheads to compute and disseminate the transmission schedule in addition to ensuring global time synchronisation. Affording these overheads is not straight forward with LoRaWAN restrictions on radio duty-cycle and downlink availability. Therefore, in this work, we propose TS-LoRa, an approach that tackles these overheads by allowing devices to self-organise and determine their slot positions in a frame autonomously. In addition to that, only one dedicated slot in each frame is used to ensure global synchronisation and handle acknowledgements. Our experimental results with 25 nodes show that TS-LoRa can achieve more than 99% packet delivery ratio even for the most distant nodes. Moreover, our simulations with a higher number of nodes revealed that TS-LoRa exhibits a lower energy consumption than the confirmable version of LoRaWAN while not compromising the packet delivery ratio.
http://hdl.handle.net/10468/9722

Using domain knowledge for interpretable and competitive multi-class human activity recognition

Using domain knowledge for interpretable and competitive multi-class human activity recognition Scheurer, Sebastian; Tedesco, Salvatore; Brown, Kenneth N.; O'Flynn, Brendan Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.
http://hdl.handle.net/10468/9759

How do companies collaborate in open source ecosystems? An empirical study of OpenStack

How do companies collaborate in open source ecosystems? An empirical study of OpenStack Zhang, Yuxia; Zhou, Minghui; Stol, Klaas-Jan; Wu, Jianyu; Jin, Zhi OpenSourceSoftware (OSS) has come to play a critical role in the software industry. Some large ecosystems enjoy the participation of large numbers of companies, each of which has its own focus and goals. Indeed, companies that otherwise compete, may become collaborators within the OSS ecosystem they participate in. Prior research has largely focused on commercial involvement in OSS projects, but there is a scarcity of research focusing on company collaborations within OSS ecosystems. Some of these ecosystems have become critical building blocks for organizations worldwide; hence, a clear understanding of how companies collaborate within large ecosystems is essential. This paper presents the results of an empirical study of the Open Stack ecosystem, in which hundreds of companies collaborate on thousands of project repositories to deliver cloud distributions. Based on a detailed analysis, we identify clusters of collaborations, and identify four strategies that companies adopt to engage with the Open Stack ecosystem. We also find that companies may engage in intentional or passive collaborations, or may work in an isolated fashion. Further, we find that a company’s position in the collaboration network is positively associated with its productivity in Open Stack. Our study sheds light on how large OSS ecosystems work, and in particular on the patterns of collaboration within one such large ecosystem.
http://hdl.handle.net/10468/9616

Computational Commensality: from theories to computational models for social food preparation and consumption in HCI

Computational Commensality: from theories to computational models for social food preparation and consumption in HCI Niewiadomski, Radoslaw; Ceccaldi, Eleonora; Huisman, Gijs; Volpe, Gualtiero; Mancini, Maurizio Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.
http://hdl.handle.net/10468/9382

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