SLICES-RI is being developed as a scientific instrument, following established methodologies and best practices from the scientific community. This approach is increasingly essential to address the accelerating pace of data-driven research, which continuously generates a deluge of publications. The core mission of a scientific instrument is to provide a standardized and trusted reference, enabling direct performance comparisons of algorithms and ensuring reproducibility — thereby simplifying and strengthening the peer review process. To meet these goals, SLICES-RI adopts an intent-based design and generates tailored Blueprints for specific scientific questions. While it does not aim to be exhaustive, this approach significantly reduces the complexity involved in designing and executing experiments. In this presentation, we share the current deployment status of SLICES-RI as it entered its pre-operational phase. We illustrate its capabilities through the Post-5G and Federated Learning Blueprints. Additionally, we highlight MRS/DMI, a key feature of SLICES-RI, which offers an advanced data management framework aligned with the FAIR principles and fully integrated into the experimental workflow.

Data Analytics is the art of turning data into insights for efficient and effective business decisions. Data visualization is among the most powerful tools in the data analyst’s arsenal, enabling the transformation of data into effective visualizations that can be easily comprehended. However, its effectiveness is often affected by the data analysts’ experience and their ability to quickly understand and interpret information. Even though business analytics tools have made a significant progress to deliver immersive data visualization environments for improving users’ efficiency and effectiveness, they still do not consider the individual differences in the core process that influences the visualization structure, encoding, and readability.

A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds . However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds , producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology.

Hyper-responsive mobile applications, such as augmented reality and online games, require ultra-low latency access to back-end services and data at runtime. While fog computing tries to meet such latency requirements by placing corresponding back-end services and data closer to clients, for e.g., within an access network, assuming a fixed back-end server throughout execution is problematic due to user mobility. A more flexible approach is thus required to allow for adapting to changes in network conditions when users roam, by relocating back-end services and data to closer available infrastructure. Support for real-time migration of software services exists, however, migrating associated disk state remains a bottleneck. This paper presents FOGFS, a fog file system that employs intelligent snapshotting, migration and synchronization mechanisms to speed up the migration of an application`s disk state between different edge locations at runtime. The experimental evaluation of our prototype implementation reveals that the attainable speed-up is as much as 3. 3 × compared to conventional migration approaches.

The uptake of wearable technology suggests that the time is ripe to explore new opportunities for improving mobile experiences. Apps, however, are not keeping up with the pace of technological advancement because wearables are treated as standalone devices, although their individual capabilities better classify them as peripherals with complementary roles. We foresee that the next generation of apps will orchestrate multiple wearable devices to enhance mobile user experiences. However, currently there is limited support for combining heterogeneous devices. This paper introduces Multi-Wear, a platform to scaffold the development of apps that span multiple wearables. It demonstrates experimentally how MULTI-WEAR can help bring changes to mobile apps that go beyond conventional practices.

Today there is a high demand for computing programmers, and at the same time a shortage of skilled professionals. This has triggered the creation of many initiatives in the past few years, with the aim of reversing the phenomenon. To achieve this, such events are designed to promote a more appealing image for programming, both as a profession and as a skill. This paper describes one such initiative, which uses a unique blend of differently themed, parallel workshops to motivate high school students to learn programming. With the use of questionnaires, we survey the participants and present our findings concerning the effectiveness of these workshops to engage the participants, to promote the value of coding, and to encourage the participants to consider a career in the field. We evaluate our results both at a general level, as well as by comparison among five individually themed workshops.

This paper describes the design and implementation of an educational game, called CodeAdventure, which is an adventure game for learning introductory programming concepts. CodeAdventure adopts an integrated design approach that employs various mechanisms and techniques to achieve an immersive learning experience in a fun and engaging way. CodeAdventure incorporates different learning techniques that have been shown to be effective for students' learning, such as providing hints and clues on how to solve puzzles, referencing instructional material, and providing immediate feedback on students' performance.

Drawing on Mishra and Koehler's "TPACK" model [1], we recognize that great Computer Science (CS) teaching demands great pedagogy, great technology skills and great subject knowledge. The main challenge facing any jurisdiction in implementing a CS curriculum within schools is the shortage of new teachers being trained in CS1 and to meet the demand many countries have focused on professional development (PD) for existing teachers across a diverse range of subjects. Some initial PD efforts in CS were often brief, with little follow-on support, and supported through external grant funding. As the need for CS at the K-12 level continues to grow, approaches should be sustainable and scalable. This includes preparing teachers at all levels, pre k-12 and some funding initiatives have included preservice teacher support as part of this [2]. Inside the European Union (EU) the Scientix project [3] represents a focus European Commission (EC) funded projects and is a repository for PD with resources for pedagogy, technology and subject knowledge . Besides this effort, spreading CS knowledge has been supported by volunteers led movements like CoderDojo . The panelists will discuss the following main topics: 1) Pre-service vs in-service, where do teachers come from? 2) Interdisciplinary ways of infusing CS. 3) Ways of replicating across countries CS teacher training initiatives aimed at sustaining and growing the number and quality of both in-service and pre-service teachers able to teach CS effectively. 4) Effective approaches to help teachers to build confidence in their ability to teach CS.

The proliferation of wearable and smartphone devices with embedded sensors has enabled researchers and engineers to study and understand user behavior at an extremely high fidelity, particularly for use in industries such as entertainment, health, and retail. However, identified user patterns are yet to be integrated into modern systems with immersive capabilities, such as VR systems, which still remain constrained by limited application interaction models exposed to developers. In this paper, we present Smart VR, a platform that allows developers to seamlessly incorporate user behavior into VR apps. We present the high-level architecture of Smart VR, and show how it facilitates communication, data acquisition, and context recognition between smart wearable devices and mediator systems (e.g., smartphones, tablets, PCs). We demonstrate Smart VR in the context of a VR app for retail stores to show how it can be used to substitute the requirement of cumbersome input devices (e.g., mouse, keyboard) with more natural means of user-app interaction (e.g., user gestures such as swiping and tapping) to improve user experience.

In this paper we present a comprehensive architecture to carry out experimental repeatability studies on clusters of smartphones. Our architecture is founded on Smart Lab, our in-house architecture for managing real and virtual smartphones via an intuitive Web user interface. Our presented architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner, as well as executing mockup experiments by "feeding" the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our test bed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.

The bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smart phone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined Smart Opt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner's smart phones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft's Geolife project and social patterns derived by DBLP. Our study reveals that Smart Opt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

This demo presents a graphical user interface and ranking system, coined KSpot, for effectively monitoring the K highest-ranked answers to a query Q in a wireless sensor network. KSpot deploys state-of-the-art distributed top-k query processing algorithms in order to realize both snapshot queries and historic queries minimizing the consumption of system resources and prolonging the lifetime of the deployed sensor network. Additionally, KSpot is user-friendly and customizable featuring an intuitive user interface that enables a user to express declarative SQL-like queries over any ad-hoc scenario and to display the results graphically as opposed to the traditional tabular representation. To demonstrate the applicability of our system during the conference, we will continuously identify the K conference rooms with the highest sound level and display them such that conference attendees will be able to quickly determine the rooms with the most active discussions. The demo will also allow attendees to customize the system by changing the target scenario (e.g., by adapting the K value, the sensed parameter, etc.) Finally, we will present KSpot's system panel which continuously displays the savings in energy and messages that our system yields.

Continuous queries in wireless sensor networks (WSNs) are founded on the premise of query routing tree structures (denoted as T), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the energy-driven tree construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network.

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60% failure then we can recover over 80% of generated events exactly.

Continuous queries in wireless sensor networks are established on the premise of a routing tree that provides each sensor with a path over which answers can be transmitted to the query processor. We found that these structures are sub- optimality constructed in predominant data acquisition systems leading to an enormous waste of energy. In this paper we present MicroPulse1, a workload-aware optimization algorithm for query routing trees in wireless sensor networks. Our algorithm is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method. A node S utilizes this information in order to locally derive the time instance during which it should wake up, the interval during which it should deliver its workload and the workload increase tolerance of its parent node. We additionally provide an elaborate description of energy-conscious algorithms for disseminating and maintaining the critical path cost in a distributed manner. Our trace-driven experimentation with real sensor traces from Intel Research Berkeley shows that MicroPulse can reduce the data acquisition costs by many orders.

In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.

In this paper we present MicroPulse, a novel framework for adapting the waking window of a sensing device S based on the data workload incurred by a query Q. Assuming a typical tree-based aggregation scenario, the waking window is defined as the time interval r during which S enables its transceiver in order to collect the results from its children. Minimizing the length of r enables S to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. Our method is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the Critical Path Method. We show through trace- driven experimentation with a real dataset that MicroPulse can reduce the energy cost of the waking window by three orders of magnitude.