In a long-term commitment to designing for the aesthetics of human–drone interactions, we have been troubled by the lack of tools for shaping and interactively feeling drone behaviours. By observing participants in a three-day drone challenge, we isolated components of drones that, if made transparent, could have helped participants better explore their aesthetic potential. Through a bricolage approach to analysing interviews, field notes, video recordings, and inspection of each team’s code, we describe how teams 1) shifted their efforts from aiming for seamless human–drone interaction, to seeing drones as fragile, wilful, and prone to crashes; 2) engaged with intimate, bodily interactions to more precisely probe, understand and define their drone’s capabilities; 3) adopted different workaround strategies, emphasising either training the drone or the pilot. We contribute an empirical account of constraints in shaping the potential aesthetics of drone behaviour, and discuss how programming environments could better support somaesthetic perception–action loops for design and programming purposes.

This paper introduces the design of a data pipeline system (DPS) integrated with artificial intelligence (AIF) functions to support continuous AI learning and operations for network automation in 5G/6G systems. We design the DPS as a chain of functions, namely ingress and egress Network Data Broker Function (iNDBF and eNDBF) and Network Data Preprocessing Function (NDPPF), to support in-network learning operations. To take into account the distributed nature of the network architecture of 5G systems and beyond, we conceive the DPS to be integrated seamlessly with a distributed learning frameworks such as the federated learning (FL). We performed a realistic evaluation, employing a real dataset from a national mobile operator to simulate the network architecture. Additionally, a FL framework for anomaly detection is integrated with the DPS to assess the effectiveness of our proposal. Evaluation results show that delays in end-to-end data transmission and preprocessing to the AIF locations can cause distributed learning AIFs to work with stale data. The results also highlight how the DPS can counterbalance these delays leading to desynchronisation of the distributed learning process, bringing to AIFs with higher accuracy.

We present a novel intercorporeal experience – an intersubjective haptic voice. Through an autobiographical design inquiry, based on singing techniques from the classical opera tradition, we created Corsetto, a kinesthetic garment for transferring somatic reminiscents of vocal experience from an expert singer to a listener. We then composed haptic gestures enacted in the Corsetto, emulating upper-body movements of the live singer performing a piece by Morton Feldman named Three Voices. The gestures in the Corsetto added a haptics-based ‘fourth voice’ to the immersive opera performance. Finally, we invited audiences who were asked to wear Corsetto during live performances. Afterwards they engaged in micro-phenomenological interviews. The analysis revealed how the Corsetto managed to bridge inner and outer bodily sensations, creating a feeling of a shared intercorporeal experience, dissolving boundaries between listener, singer and performance. We propose that ‘intersubjective haptics’ can be a generative medium not only for singing performances, but other possible intersubjective experiences.

Ensuring two battery-free devices discover each other to start communication is challenging due to intermittent and unpredictable energy availability. In this abstract, we exploit ultra-low power channel sensing to enable efficient neighbor discovery. Preliminary results show our method is promising in various ambient energy scenarios.

Cooperative intelligent transport systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100 percent availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

In this paper we present our late-breaking work in leveraging a soft robotic fiber-based wearable system for the transposition of somatic knowledge and experience within the context of singing. We examine how the transposition of the physical nuances of singing from one body to another, or multiple other bodies, is possible by engaging with a soma design process. We share our findings in the context of experience transposition, resulting in a preliminary prototype: a pneumatically controlled soft robotic garment—called ADA (short for air-driven actuator) for re-enacting felt experiences of singing onto the human body. We contribute with 1) our initial findings in transposing singing experiences between and across bodies, and 2) a preliminary wearable robotic garment to mediate intersomatic experiences of singing.

Evangelia Gogoulou, Magnus Boman, Fehmi Ben Abdesslem, Nils Hentati Isacsson, Viktor Kaldo, Magnus Sahlgren. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

GPS sensors have been widely used to track people's everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.

This paper outlines the tradeoffs involved in utilizing Information-Centric Networking (ICN) for Internet of Things (IoT) scenarios. It describes contexts and applications where the IoT would benefit from ICN, and where a host-centric approach would be better. Requirements imposed by the heterogeneous nature of IoT networks are discussed in terms of connectivity, power availability, computational and storage capacity. Design choices are then proposed for an IoT architecture to handle these requirements, while providing efficiency and scalability. An objective is to not require any IoT specific changes of the ICN architecture per se, but we do indicate some potential modifications of ICN that would improve efficiency and scalability for IoT and other applications.

Exploring a large area can be conveniently performed by a team of small autonomous vehicles for different applications, such as search and rescue, cleaning, or lawn mowing. The efficiency and performance of such autonomous exploration depends on the exploration algorithm implemented by the vehicles, and can be enhanced with a better communication and collaboration strategy within the team. In this paper, a new algorithm is proposed and evaluated where vehicles with a limited communication range pro-actively seek their teammates to exchange information about the explored area. Simulations show that this approach allows the vehicles to finish the exploration and return to their base station 18% faster, without consuming more energy.

The number of mobile devices is estimated to now exceed the world's population, using more and more cloud services, and hence generating more and more traffic. Smartphones generate 95% of the total global handset traffic, and while approximately half of this traffic is sent to cellular networks, other handsets such as tablets are also using increasingly the cellular networks. This paper provides a closer look at the traffic generated on cellular networks by exploring billions of HTTP requests sent by millions of users to a nation-wide cellular network during 41 days. We confirm that - as in many other contexts - 20% of the users are responsible for more than 80% of the requests and provide a deeper analysis of the cellular network usage. Furthermore, we characterise the activity of users on their mobile device and which cloud services they use. For instance, almost 30% of the users use the cellular network frequently, mainly using search services and social networks, but 20% of their requests are sent to advertisement and tracking systems.

Mobile devices are now part of our everyday lives, and the emergence of online application marketplaces allow a rapid spread of new mobile applications to a large user base. Such user-installed mobile applications constitute a large part of our daily interaction with the devices. With more than one million available applications, Android Market, the online catalog for Android devices allows users to choose and download a large selection of disparate applications. Analysing and characterising the application marketplace download patterns provides a better insight on the needs and behaviour of users In this paper, we explore a large dataset collected by a major European telecom operator to study the downloads of Android applications on a nationwide scale. Our findings include that more than 43% of the application data downloaded is for games, and that a set of only 10 GB of applications is responsible for 88% of the 45 TB downloaded in total by all the users.

Information-Centric Networking (ICN) is an alternative architecture for computer networks, where the communication is focused on the data being transferred instead of the communicating hosts. This paper describes a demo of an experience sharing application for mobile phones built on an ICN platform designed for devices with intermittent connectivity. In particular, we detail how this application will be showcased in an indoor exhibition where experience is shared with media content that is geo-tagged using Bluetooth beacons and spread opportunistically to other users.

Traffic from wireless and mobile devices is expected to soon exceed traffic from fixed devices. Understanding the behaviour of users on mobile devices is important in order to improve the offered services and the provision of the underlying network. Globally, more than 60% of consumer Internet traffic is estimated to be video traffic, and the most popular video website, YouTube, estimates that mobile access makes up nearly 40% of the global watch time. This paper presents the first work to study the characteristics of YouTube user requests on a nationwide cellular network. This study is based on the analysis of a large dataset generated by 3 million users and collected by a major telecom operator. We show for instance that 20% of the users generate 78% of the requests, and that over 80% of the requests target only 20% of the distinct videos accessed during the data collection period. Our results provide a comprehensive insight into the way people use YouTube on mobile devices, and show a very high potential for video cacheability on the cellular network.

An efficient way to bound the size of clusters in large-scale self-organizing wireless networks is to rely on a budget-based strategy. The side effect of conventional budget- based clustering approaches is that they generate a potentially large number of small, even single-node, clusters. The consequence is that while clusters are bounded, their average size may be far from the expected value (the budget), which negatively impacts the performance of the communication systems running on top of it. In contrast, we propose Fair and Flexible Budget- Based Clustering (FFBC) to form size-controlled clusters in large-scale self-organizing networks. For a given target cluster size, our approach outperforms previous budget-based algorithms by creating clusters of average size closer to the requested value and avoiding isolated nodes.