Roadmap for Real World Internet applications

This paper emphasises the socioeconomic background required to design the Future Internet in order that its services will be accepted by its users and that the economic value latent in the technology is realised. It contains an innovative outlook on sensing aspects of the Future Internet and describes a scenario-based design approach that is feasible to roadmap the dynamic deployment of Real World Internet applications. A multifaceted socioeconomic assessment leads to recommendations for the technology deployment and key features of the Future Internet that will globally integrate technologies like Wireless Sensor and Actuator Networks and Networked Embedded Devices.Real World Internet ; Future Internet ; Scenario-based Design ; Socioeconomics ; Business Models ; Requirements

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Integrated Scenario-based Design Methodology for Collaborative Technology Innovation

The paper presents a scenario-based methodology developed and tested throughout cooperative research and development projects. It is aimed at supporting information technology innovation with an end-to-end Human and Social Sciences assistance. This methodology provides an integrated approach combining a vision of the potential users, business aspects and technological challenges throughout the design process. An original combination of different methods is proposed and experimented: user-centred design, scenario-based design, user and functional requirements analysis, business value analysis, user acceptance studies, and visualization methods. This methodology has been implemented in three European R&D projects, in the domain of the telecommunications and Internet infrastructure. The key contributions of this approach are that it unifies brings together visions of the users, potential business value and technology challenges thanks to scenario construction.Scenario-based design ; user requirements ; business economics ; functional requirements ; visualization

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines