The Internet of Things (IoT) involves intelligent, heterogeneous, autonomous and often distributed things which interact and collaborate to achieve common goals. A useful concept for supporting this effort is Emergent Configuration (EC), which consists of a dynamic set of things, with their functionalities and services, that cooperate temporarily to achieve a goal. In this paper we introduce a commitment-based approach that exploits the concept of commitments to realize ECs. More specifically, (i) we present a conceptual model for commitment-based ECs, (ii) we use the smart meeting room scenario to illustrate how ECs are realized via commitments.

The Internet of Things (IoT) has a great potential to change our lives. Billions of heterogeneous, distributed, intelligent, and sometimes mobile devices, will be connected and offer new types of applications and ways to interact. The dynamic environment of the IoT, the involvement of the human in the loop, and the runtime interactions among devices and applications, put additional requirements on the systems' architecture. In this paper, we use the Emergent Configurations (ECs) concept as a way to engineer IoT systems and propose an architecture for ECs. More specifically, we discuss (i) how connected devices and applications form ECs to achieve users' goals and (ii) how applications are run and adapted in response to runtime context changes including, e.g., the sudden unavailability of devices, by exploiting the Smart Meeting Room case.

Systems of Systems (SoS) and the Internet of Things (IoT) have many common characteristics. For example, their constituents are heterogeneous, often autonomous, and distributed. Moreover, both IoT systems and SoS achieve their intended goals by means of the dynamic collaboration and coordination among their constituents. In this paper, by using the notion of Emergent Configurations (ECs) as a means to engineer IoT systems, we show how ECs in the IoT can be regarded both as systems and SoS by exploiting two scenarios.

Systems of Systems(SoS) and theInternet of Things(IoT)have many common characteristics. For example, their constituents are heterogeneous, autonomous and often distributed. Moreover, both IoT and SoS achieve intended goals by means of the highly dynamic cooperation among their constituents. In this paper we study the relation between IoT and SoS. We discuss the characteristics of both concepts and highlight common aspects. Furthermore, we introduce the conceptSystem of Emergent Configurations (SoECs) to describe IoT-based SoS.

A security-enhanced agile software development process, SEAP, is introduced in the development of a mobile money transfer system at Ericsson Corp. A specific characteristic of SEAP is that it includes a security group consisting of four different competences, i.e., Security manager, security architect, security master and penetration tester. Another significant feature of SEAP is an integrated risk analysis process. In analyzing risks in the development of the mobile money transfer system, a general finding was that SEAP either solves risks that were previously postponed or solves a larger proportion of the risks in a timely manner. The previous software development process, i.e., The baseline process of the comparison outlined in this paper, required 2.7 employee hours spent for every risk identified in the analysis process compared to, on the average, 1.5 hours for the SEAP. The baseline development process left 50% of the risks unattended in the software version being developed, while SEAP reduced that figure to 22%. Furthermore, SEAP increased the proportion of risks that were corrected from 12.5% to 67.1%, i.e., More than a five times increment. This is important, since an early correction may avoid severe attacks in the future. The security competence in SEAP accounts for 5% of the personnel cost in the mobile money transfer system project. As a comparison, the corresponding figure, i.e., For security, was 1% in the previous development process.

With the support of the regional public transport operator, this paper explores the potential for mobile games to make journeys on public transport more enjoyable. To this end we have developed a game called Bus Runner which is a context-aware endless runner, based on open and shared data. By blending features of the physical world, such as recognisable landmarks, with the game’s virtual world, we situate and enhance passengers’ experience of travelling on public transport. We identify a set of challenges and opportunities based on the development and evaluation of Bus Runner. These are of relevance not only for game development purposes, but also impact context-driven content generation of infotainment services as a whole.

Agile software development is increasingly adopted by companies evolving and maintaining software products to support better planning and tracking the realization of user stories and features. While convincing success stories help to further spread the adoption of Agile, mechatronics-driven companies need guidance to implement Agile for non-software teams. In this comparative case study of three companies from the Nordic region, we systematically investigate expectations and challenges from scaling Agile in organizations dealing with mechatronics development by conducting on-site workshops and surveys. Our findings show that all companies have already successfully implemented Agile in their software teams. The expected main benefit of successfully scaling agile development is a faster time-to-market product development; however, the two main challenges are: (a) An inflexible test environment that inhibits fast feedback to changed or added features, and (b) the existing organizational structure including the company’s mind-set that needs to be opened-up for agile principles.

As Marc Andreessen said in his Wall Street Journal OpEd, software is eating the world. The systems that we are building today and in the near future will exhibit levels of autonomy that will put new demands on the engineering of such systems. Although promising examples of autonomous systems exist, there is no established methodology for systematically building autonomous systems that employ modern software engineering technology such as continuous deployment and data-driven engineering. The contribution of this paper is twofold. First, it identifies and presents the challenge of continuous evolution of autonomous systems as a well-defined problem that needs to be addressed by software engineering research. Second, it presents a conceptual solution to this problem that integrates the development of new software for autonomous systems by R&D teams with systematic experimentation by autonomous systems.

Smart connected home systems aim to enhance the comfort, convenience, security, entertainment, and health of the householders and their guests. Despite their advantages, their interconnected characteristics make smart home devices and services prone to various cybersecurity and privacy threats. In this paper, we analyze six classes of malicious threat agents for smart connected homes. We also identify four different motives and three distinct capability levels that can be used to group the different intruders. Based on this, we propose a new threat model that can be used for threat profiling. Both hypothetical and real-life examples of attacks are used throughout the paper. In reflecting on this work, we also observe motivations and agents that are not covered in standard agent taxonomies.

Mission-critical Internet of Things (MC-IoT) systems involve heterogeneous things from both the digital and physical worlds. They run applications whose failure might cause significant and possibly dramatic consequences, such as interruption of public services, significant business losses, and deterioration of enterprise operations. These applications require not only high availability, reliability, safety, and security but also regulatory compliance, scalability, and serviceability. At the same time, they're exposed to various facets of uncertainty, spanning from software and hardware variability to mission planning and execution in possibly unforeseeable environments. Model-driven engineering can potentially meet these challenges and better enable the adoption of MC-IoT systems.

The Internet of Things (IoT) unleashes great opportunities to improve our way of living and working through a seamless and highly dynamic cooperation among heterogeneous things including both computer-based systems and physical objects. However, properly dealing with the design, development, deployment and runtime management of IoT applications means to provide solutions for a multitude of challenges related to intelligent distributed systems within the IoT. In this paper we propose Model-Driven Engineering (MDE) as a key enabler for applications running on intelligent distributed loT systems. MDE helps in tackling challenges and supporting the lifecycle of such systems. Specifically, we introduce MDE4IoT, an MDE approach enabling the modelling of things and supporting intelligence as self-adaptation of Emergent Configurations in the IoT. Moreover, we show how MDE, and in particular MDE4IoT, can help in tackling several challenges by providing the Smart Street Lights concrete case.

In this paper we present our proposal for improving education with hands-on, project-based and experimental scenarios for engineering students with the use of learning analytics. We accomplish this through teacher and learner engagement, user studies and evaluated trials, performed at UCV (University of Craiova, Romania) and DTU (Technical University of Denmark). The PELARS project (Practice-based Experiential Learning Analytics Research And Support) provides technological tools and ICT-based methods for collecting activity data (moving image-based and embedded sensing) for learning analytics (data-mining and reasoning) of practice-based and experiential STEM. This data is used to create analytics support tools for teachers, learners and administrators, providing frameworks for evidence-based curriculum design and learning systems. The PELARS project creates behavioral recording inputs, proving a new learning analytic that is scalable in application, and bridge qualitative and quantitative methods through reasoning and feedback from input data. The project serves to better understand learners' knowledge in physical activities in laboratory and workshop environments, as well as informal learning scenarios. PELARS traces and helps assess learner progress through technology enhancement, in novel ways building upon current research. The project results in learning analytics tools for practice-based STEM learning that are appropriate for real-world learning environments.

Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.

We investigate the opportunities and challenges of the forth wave of digitalization, also referred to as the Internet of Things (IoT), with respect to public transport and how it can support sustainable development of society. Environmental, economical, and social perspectives are considered through analysis of the existing literature and explorative studies. We conclude that there are great opportunities for both transport operators and planners, as well as for the travelers. We describe and analyze a number of concrete opportunities for each of these actors. However, in order to realize these opportunities, there are also a number of challenges that needs to be addressed. There are both technical challenges, such as data collection issues, interoperability, scalability and information security, and non-technical challenges such as business models, usability, privacy issues, and deployment.

Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulation of complex systems promises to enable us to understand how a human nervous system and brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.

Social phenomena simulation in the area of agent-based modeling and simulation concerns the emulation of the individual behavior of a group of social entities, typically including their cognition, actions, and interaction.

This book constitutes the refereed proceedings of the 15th International Conference on Practical Applications of Scalable Multi-Agent Systems, PAAMS 2017, held in Porto, Portugal, in June 2017.The 11 revised full papers, 11 short papers, and 17 Demo papers were carefully reviewed and selected from 63 submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources.  

In our connected world, recommender services have become widely known for their ability to provide expert and personalize information to participants of diverse applications. The excessive growth of social networks, a new kind of services are being embraced which are termed as "group based recommendation services", where recommender services can be utilized to discover sub-communities within implicit social groups and provide referrals to new participants in order to join various sub-communities of other participants who share similar preferences or interests. Nevertheless, protecting participants' privacy in recommendation services is a quite crucial aspect which might prevent participants from exchanging their own data with these services, which in turn detain the accuracy of the generated referrals. So in order to gain accurate referrals, recommendation services should have the ability to discover previously unknown sub-communities from different social groups in a way to preserve privacy of participants in each group. In this paper, we present a middleware that runs on end-users' mobile phones to sanitize their profiles' data when released for generating referrals, such that computation of referrals continues over the sanitized version of their profiles' data. The proposed middleware is equipped with cryptography protocols to facilitate private discovery of sub-communities from the sanitized version of participants' profiles in a university scenario. Location data are added to participants' profiles to improve the awareness of surrounding sub-communities, so the offered referrals can be filtered based on adjacent locations for participant's location. We performed a number of different experiments to test the efficiency and accuracy of our protocols. We also developed a formal model for the tradeoff between privacy level and accuracy of referrals. As supported by the experiments, the sub-communities were correctly identified with good accuracy and an acceptable privacy level.

Emotions and physical health are strongly related. A first step towards emotional well-being is to monitor, understand and reflect upon one’s feelings and emotions. A number of personal emotion-tracking applications are available today. In this paper we describe an examination of these applications which indicates that many of the applications do not provide sufficient support for monitoring a full spectrum of emotional data or for analyzing or using the data that is provided. To design applications that better support emotional well-being, the full capabilities of the Internet of Things should be utilized. The paper concludes with a description of how Internet of Things technologies can enable the development of systems that can more accurately capture emotional data and support personal learning in the area of emotional health.

In many companies, product management struggles in getting accurate customer feedback. Often, validation and confirmation of functionality with customers takes place only after the product has been deployed, and there are no mechanisms that help product managers to continuously learn from customers. Although there are techniques available for collecting customer feedback, these are typically not applied as part of a continuous feedback loop. As a result, the selection and prioritization of features becomes far from optimal, and product deviates from what the customers need. In this paper, we present a literature review of currently recognized techniques for collecting customer feedback. We develop a model in which we categorize the techniques according to their characteristics. The purpose of this literature review is to provide an overview of current software engineering research in this area and to better understand the different techniques that are used for collecting customer feedback.

Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.

Requirements gathering are an important aspect of application development, especially when users are people with special needs. Traditionally, this process is being conducted using conventional methods, such as interviews, workshops and questionnaires. These approaches, however, are unable to grasp the full context when collecting data from the communities of people with special needs, mainly because of the difficult access to participants and incomprehensiveness of the data gathered. To mitigate such issues, in this position paper, we argue that existing traditional methods could be complemented by means of Internet of Things. The immense amount of data gathered from various devices interconnected could help generate meaningful data that will complement the usually insufficient amount collected using traditional methods. This new approach is, however, associated with challenges that are discussed along with a possible scenario on how data complementing from traditional and the indirect method could be done.

Ubiquitous computing environments enable workers to easily transition between work and non-work activities regardless of location. While this blurring of boundaries can be viewed as having a negative impact on work-life balance through the intrusion of work activities on home, family and leisure time (Clark, 2000; Davis, 2000), a positive aspect has also been found wherein technology, particularly the smartphone, enables workers to have more control of their schedules and to remain connected to family and friends during work time (Wajcman, et al., 2008). While technology has made this duality possible, we have not yet fully taken advantage of the potential of technology to facilitate the integration of these activities. “Currently the design of the ubiquitous computing environments for home and work are each dedicated to specific realms and often fail to take into consideration the requirements to integrate activities across these two life spaces” (Cousins & Varshney, 2009, p. 123). To better support workers in managing their transitions between work and non-work activities we suggest that context aware computing environments utilizing the Internet of Things may offer a viable solution.

Future software systems will be highly dynamic. We are already experiencing, for example, a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self-adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.

In order to support equity and access in collaborative learning, it is important to understand the nature of collaborative learning itself. One approach is to look at the physical aspects of how students collaborate while engaged in open-ended group-work during Practice-Based Learning (PBL) activities. By analysing how students and teachers move and interact in relation to each other, the space they are in and the objects within it, we can gain a greater understanding of the physical nature of collaborative group-work. This understanding can help us to create a learning environment that intrinsically but unobtrusively supports access by all user profiles who seek to engage with it, thus promoting equity of engagement and participation. Using the example of the design of a Learning Analytics System (LAS) and the educational furniture in which it is implemented, we will show how the physical design of a CSCL implementation can support increased collaboration.

In Schön’s account, contingency is encountered in the essential reflective dialogue with the design situation, in move-making, reflection and move-making again. Design moves are concrete, yet also speculative in that their outcome cannot be fully ascertained a priori. Once performed, we may be in a position to judge whether the outcome is aligned with our intentions, or whether something rather different was produced. Unexpected results can be revealing and inspiring, or close down exploration, shutting off further lines of inquiry. Designers may operate in a loose, experimental manner and by definition outcomes will have a greater degree of unexpectedness. Importantly, however, the designer does not intend for a specific outcome, but rather intends for experimentation, guided only by loose framing. Un- expected results cannot be considered a mistake or error under these conditions because that is precisely what is intended. We seek to diminish the event and significance of ‘mistakes’ and ‘errors’, and argue instead that experimental practice is to operate with a looser ‘grip’ on the design situation. We contrast this with non-experimental design practice and develop a phenomenologically-informed account of design practice.

We present a study, where we used regression in order to predict the number of bicycles registered by a bicycle counter (located in Malmö, Sweden). In particular, we compared two regression problems, differing only in their target variables (one using the absolute number of bicycles as target variable and the other one using the deviation from a long-term trend estimate of the expected number of bicycles as target variable). Our results show that using the trend curve deviation as target variable has potential to improve the prediction accuracy (compared to using the absolute number of bicycles as target variable). The results also show that support vector regression (using 2nd and 3rd degree polynomial kernels) and regression trees perform best for our problem.

Macro-level models is the dominating type of freight transport analysis models for supporting the decision-making in public authorities. Recently, also agent-based models have been used for this purpose. These two model types have complementing characteristics: macro-level models enable to study large geographic regions in low level of detail, whereas agent-based models enable to study entities in high level of detail, but typically in smaller regions. In this paper, we suggest and discuss three approaches for combining macro-level and agent-based modeling: exchanging data between models, conducting supplementary sub-studies, and integrating macro-level and agent-based modeling. We partly evaluate these approaches using two case studies and by elaborating on existing freight transport analysis approaches based on executing models in sequence.

Enforcing security in Internet of Things environments has been identified as one of the top barriers for realizing the vision of smart, energy-efficient homes and buildings. In this context, understanding the risks related to the use and potential misuse of information about homes, partners, and end-users, as well as, forming methods for integrating security-enhancing measures in the design is not straightforward and thus requires substantial investigation. A risk analysis applied on a smart home automation system developed in a research project involving leading industrial actors has been conducted. Out of 32 examined risks, 9 were classified as low and 4 as high, i.e., most of the identified risks were deemed as moderate. The risks classified as high were either related to the human factor or to the software components of the system. The results indicate that with the implementation of standard security features, new, as well as, current risks can be minimized to acceptable levels albeit that the most serious risks, i.e., those derived from the human factor, need more careful consideration, as they are inherently complex to handle. A discussion of the implications of the risk analysis results points to the need for a more general model of security and privacy included in the design phase of smart homes. With such a model of security and privacy in design in place, it will contribute to enforcing system security and enhancing user privacy in smart homes, and thus helping to further realize the potential in such IoT environments.

Achieving security in Internet of Things environments has been identified as one of the top barriers for realizing the vision of smart, energy-efficient homes and buildings. In this context, understanding the risks related to the use and potential misuse of information about customers, partners, and end-users, as well as, forming methods for integrating security-enhancing measures in the design is not straightforward and thus requires substantial investigation. A risk analysis applied on a smart home automation system developed in a research project involving leading industrial actors has been conducted. The results indicate that with the implementation of standard security features, new as well as, current risks can be minimized to acceptable levels albeit that the most serious risks, i.e., those derived from the human factor, need more careful consideration, as they are inherently complex in nature.

The domain of smart home environments is viewed as a key element of the future Internet, and many homes are becoming “smarter” by using Internet of Things (IoT) technology to improve home security, energy efficiency and comfort. At the same time, enforcing privacy in IoT environments has been identified as one of the main barriers for realizing the vision of the smart home. Based on the results of a risk analysis of a smart home automation system developed in collaboration with leading industrial actors, we outline the first steps towards a general model of privacy and security for smart homes. As such, it is envisioned as support for enforcing system security and enhancing user privacy, and it can thus help to further realize the potential in smart home environments.

In this paper, we present our body-and-mind-centric approach for the design of wearable personal assistants (WPAs) motivated by the fact that such devices are likely to play an increasing role in everyday life. We also report on the utility of such a device for orthopedic surgeons in hospitals. A prototype of the WPA was developed on Google Glass for supporting surgeons in three di↵erent scenarios: (1) touch-less interaction with medical images, (2) tele-presence during surgeries, and (3) mobile access to Electronic Patient Records (EPR) during ward rounds. We evaluated the system in a clinical simulation facility and found that while the WPA can be a viable solution for touch-less interaction and remote collaborations during surgeries, using the WPA in the ward rounds might interfere with social interaction between clinicians and patients. Finally, we present our ongoing exploration of gaze and gesture as alternative input modalities for WPAs inspired by the hospital study.

In this position paper we stress the need for considering the nature of human attention when designing future potentially interruptive IoT and propose to let IoT devices share attention-related data and collaborate on the task of drawing human attention in order to achieve higher quality attention management with less overall system resources. Finally, we categorize some existing strategies for drawing people’s attention according to a simple symbiotic (human- machine) attention management framework.

In the context of globalization, the requirements on transport logistics continuously rise. Often goods travel through many different countries, using several transport modes and involving a number of different actors. Implementing some level of intelligence on the goods, which provide them with the capabilities to assist in the logistical activities, is one of the instruments that can be used to improve control and efficiency in transports and goods-handling. The concept of intelligent goods both opens up for new types of services and may be used to improve currently available services. The research is mainly focused on the characteristics, possible architectures, and applications of intelligent goods systems. In this context, an intelligent goods system refers to a number of interacting components, e.g. on-board units, servers, and RFID tags, which together provide intelligent goods services. Intelligent goods refer to goods with a higher degree of intelligence than just providing the ID of the goods, and generally the concept involve information processing and/or storage on or close to the goods, acting on behalf of the goods throughout the whole transport. The purpose of the studies is to investigate how intelligent goods can be used to improve goods transports in terms of more efficient goods-handling as well as better control of the goods and the transportation process, but also in terms of more efficient information sharing, e.g. between different actors. This may in turn provide reduced costs, environmental impact and usage of infrastructure. The research is concentrated on the communication and processing of information before, during and after transport. Most of the research results are applicable to ii goods transport by any mode, whereas some of the research has an emphasis on road transport. A framework is presented which can be used to describe intelligent goods systems, including the capabilities of the goods, necessary information entities related to the goods, as well as a number of primitive services that can be used as building blocks when creating more advanced intelligent goods services. Furthermore, a new approach to service description is proposed, which can be used to, amongst others, define an intelligent goods service and to perform architecture analyses. By identifying architectures corresponding to different service solutions, intelligent goods can be compared with other types of solutions, for instance more centralized approaches. In particular, different situations and services put different requirements on a system and the benefits of using intelligent goods vary. In order to investigate how intelligent goods may be applied in practice, two services have been examined in more detail: a dynamic shelf- life prediction service, and a consignment-level emission allocation service. These studies involve field tests, interviews and simulations. Finally, an investigation of how intelligent goods systems can be modelled as multi-agent systems is also included.

The durability of perishable food varies due to different storage and handling conditions during the supply chain as well as final consumer activities. If the durability of the individual products can be estimated, dynamic expiry dates may be developed and used to prevent food waste, ensure quality, and improve supply chain activities etc. Depending on the system architecture used for such a service, different qualities can be obtained in terms of usability, accuracy, security etc. This paper presents a novel approach for how to identify and select the most suitable system architectures of a dynamic expiry date service. The approach is illustrated by focusing on one of the potential user groups, the supply chain managers. The approach consists of three steps: (i) identify the potential architectures, (ii) filter out the least relevant candidates by applying a specified set of principles, and (iii) perform an analytic hierarchy process (AHP) based on a set of quality attributes.

Purpose: To investigate the possibilities, risks and requirements of a dynamic shelf life service – a technological innovation focusing on minimizing food waste in supply chains (SC). Design/methodology/approach: Semi-structured, open-ended interviews with SC actors have been used to identify the requirements, possibilities and risks with a dynamic shelf life service. Field tests have been conducted to investigate practical implications and effects of small temperature variations on shelf life. The field tests involved sensors based on Bluetooth Low Energy (BLE) and Radio Frequency Identification (RFID). Findings: The results show that a DSLP service holds great potentials. The field tests revealed that shelf life predictions are sensitive to small temperature differences along the cold chain. Results from the interviews confirm the importance of accuracy. The interviews also emphasize the importance of sharing costs among the involved actors. Research limitations/implications: Key aspects from 11 SC actors concerning dynamic shelf life prediction in cold chains are provided. The field tests involve a SC from production to household. Practical implications: Implementation of a dynamic shelf life service can increase visibility and information flow within SCs. The system can also be integrated with companies’ business systems creating new business opportunities and reducing manual work by automatically alerting quality fluctuations of food products. Original/value: Quantitative and qualitative data from 11 SC actors are provided. This information, together with the experiences reported from the field tests, have the potential to help replacing dysfunctional date labelling systems and reduce food waste.

This paper presents and evaluates a new method for how emissions from freight transport routes with single or several points of loading and unloading, can be allocated to individual consignments. The method, called Dedicated Distance Proportional Allocation (DDPA), has been developed based on a literature review, discussions with logistics providers, and analysis. DDPA is designed to have low data processing requirements and be easy to explain to actors involved. Furthermore, it supports several levels of information availability, and accounts for any set of vehicle-limiting factors, as well as prepositioning/repositioning. DDPA has been evaluated in simulations with different levels of information availability, together with three existent allocation methods: the Equal profit method (EPM), the CEN EN16258:2011 standard and the Greenhouse gas (GHG) protocol. The simulations show that the GHG protocol under-allocates the total amount of emissions, on average. EPM and DDPA achieve equal relative savings, whereas for CEN EN16258:2011 and the GHG protocol, relative savings vary, on average. When DDPA is used with low level of information availability, an error is introduced which can be reduced by applying compensation factors. Since DDPA accepts low information availability, the Intelligent Products concept can be applied for computing and storing emissions allocations, at the time of unloading. The results from this study can be used for further development and implementation of consignment allocation methods. Furthermore, by combining DDPA with other environmental load approaches for other parts of a product’s life cycle, a complete life cycle assessment of the product’s environmental impact can be obtained.

The benefits and barriers that software development companies face when moving beyond agile development practices are identified in a multiple-case study in five Finnish companies. The practices that companies need to adopt when moving towards innovation experiment systems are recognised. The background of the study is the Stairway to Heaven (StH) model that describes the path that many software development companies take when advancing their development practices. The development practices in each case are investigated and analysed in relation to the StH model. At first the results of the analysis strengthened the validity of the StH model as a path taken by software development companies to advance their development practices. Based on the findings, the StH model was extended with a set of additional practices and their adoption levels for each step of the model. The extended model was validated in five case companies.

Introducing physical computing into regular school classes is challenged by constraints of schedules and curricula structures, which do not allow for time-consuming electronics prototyping. We present a novel approach to prototyping with physical computing components with the Arduino-based TALKOO kit: It comprises hardware modules, a visual IDE and prototyping material. Sensor and actuator modules are pluggable and do not require soldering and prior knowledge in electronics. The components have the ability to "talk" back to the visual IDE and to a learning analytics system. A new paradigm for visual programming maps physical modules onto virtual representations on screen making programming more intuitive. The TALKOO kit expands the field of application of physical computing for children in regular school contexts. Preliminary evaluation results show that children were able to build elaborative prototypes within an hour.

The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.

Based on an episode from a design case, it is argued that explorative interaction design with hybrid materials combining physical and digital properties is better conceptualized as palpating the hybrid materials, rather than having a conversation with them. This leads to a proposed design strategy for designers working with hybrid materials: to build prototypes and do material experiments in a way that foregrounds immediate sensate feedback and externalizes responses that are normally not perceivable.

This document seeks new areas of research in musculoskeletal health instrumentation development, under the current context of the Internet of Things (IoT) and the design needs of achieving more efficient, profitable, and better user experience in healthcare-related products and services. Three health measurement instrumentation case studies are presented, which show latent barriers and needs as well as possible methods of solving these situations. The cases deal with instrumentation related to motion capture (MoCap), balance control measurement, and muscle strength measured by dynamometry. Using the cases, a scheme that includes the key elements involved in a health instrumentation system is proposed. The scheme is ideated to facilitate the creation of health development tools (HDT) that are intermediate tools that designers, developers, or researchers can use to implement health products and services in a more efficient, and accessible way. (c) 2017 The Authors. Published by Elsevier B.V.

Intelligent Truck Parking (ITP) involves efficient management of information as a tool for addressing some of the problems related to truck parking, such as, the inability to navigate to, or reserve a parking space. Different telematic services (ITP core services) may be required to address different stakeholder needs, e.g., a navigation service for finding the way to a truck parking area, and a reservation service for assuring the availability of a parking space when in the parking area. In addition to ITP core services, there are several other telematic services that address transport problems in general. If the synergies of different services are taken into account during design and deployment of telematic services, cost reduction may be achieved through functionality sharing. In this article, we have identified services that are relevant for sharing functionalities with ITP core services, estimate functionality costs and perform a synergy analysis to assess the marginal cost savings as a result. Overall results indicates that Traffic Information Broadcasting (TIB), Estimated Time of Arrival (ETA) for goods, and Geo-fencing (GEO) can form clusters with ITP core services that could lead to better cost savings compared to the rest of the services studied.

A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure.

Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.

The ubiquity of sensor infrastructures in urban environments poses new challenges in managing the vast amount of data being generated and even more importantly, deriving insights that are relevant and actionable to its users and stakeholders. We argue that understanding the context in which people and things are connected and interacting is of key importance to this end. In this position paper, we present ongoing work in the design of a multiagent model based on immunity theory concepts with the scope of enhancing sensor-driven architectures with context-aware capabilities. We aim to demonstrate our approach in a real-world scenario for processing streams of sensor data in a smart building

In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load-controllable devices and small-scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro-model of the Australian energy market, showing a performance improvement of about 17%.

The recent advent of ’Internet of Things’ technologies is set to bring about a plethora of heterogeneous data sources to our immediate environment. In this work, we put forward a novel concept of dynamic intelligent virtual sensors (DIVS) in order to support the creation of services designed to tackle complex problems based on reasoning about various types of data. While in most of works presented in the literature virtual sensors are concerned with homogeneous data and/or static aggregation of data sources, we define DIVS to integrate heterogeneous and distributed sensors in a dynamic manner. This paper illustrates how to design and build such systems based on a smart building case study. Moreover, we propose a versatile framework that supports collaboration between DIVS, via a semantics- empowered search heuristic, aimed towards improving their performance.

The Internet of Things (IoT) is envisioned as a global net- work of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. How- ever, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT sys- tems and applications, it is key to orchestrate heterogeneous ’things’ in a seamless, adaptive and dynamic manner, such that the system can ex- hibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users’ requirements. To this end, contextu- alisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user’s interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users’ intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.