Future generation networks will face ever increasing demands, of bandwidth and latency performance figures, to keep up with the growing requirements of a wider user base and the amount of IoT devices needing connection. Network infrastructure already depends on extensive caching and migration of resources (e.g., content delivery networks) to keep the desired contents "closer" to the user. However, as users move to different locations over their daily routines, and consequently switch network providers (home Wi-Fi, cellular on the road, work Wi-Fi, restaurant Wi-Fi, etc.), even "small" user movements might imply "large" networking differences.

The paper An Unsupervised and Non-Invasive Model for Predicting Network Resource Demands, that is due to appear on the IEEE Internet of Things Journal, develops an usupervised approach to detect "meaningful network locations" for a user, without using any additional data over what service providers already have, and allows predicting the future user locations (from the network point of view, without regard to the geographical location) and therefore optimize the required network resources. The machine learning approach is based on a conjoined clustering phase for detecting meaningful network location and their relevance in time, followed by a Markov chain model for estimating spatio-temporal transition probabilities. The work has been developed as a fruitful collaboration between members of the e-Lite group and of the CAD group at Politecnico di Torino.

Does research in Intelligent Environments satisfy the expectation of its prospective users? Are the needs and requirements of end users considered in the development of new Ambient Intelligence systems? The paper "User expectations in intelligent environments" by Fulvio Corno, published on the Journal of Reliable Intelligent Environments, explores these questions by comparing the contents of recent literature with the actual techniques and methodologies that prove that users have been involved in the research.


The definition of Intelligent Environments has always been focused around their users, aiming at helping them in a smart and transparent way, and avoiding bothering them or acting against their will. The complexity of IEs, whose technologies range from sensors to machine learning, from distributed architectures to tangible interfaces, from communication protocols to data analysis, challenges researchers from various fields to contribute innovative and effective solutions. In this quest for technical solutions to the myriad requirements of an intelligent environments, user expectations are often left behind, and while researchers tend to focus on niche technical aspects, they risk of losing the big picture of an IE "helping users in their daily life".

This paper analyzes the recent literature of the Intelligent Environments' research community, aiming at highlighting to which extent users are taken into account, or are involved, into the reported research works. Evidence shows that, while most papers refer to users in their description, only a small minority actually involve them in the design, testing or experimentation phases.

On July 11, 2018, Juan Pablo Sáenz presented the paper On The Advanced Services That 5G May Provide to IoT Applications at the audience of the 1st Annual IEEE 5G World Forum (5GWF’18), held in Santa Clara, California, USA.

The advent of the 5G network is a key enabler to the growth of IoT, with the promise to innovate and revolutionize contemporary architectures by enabling new IoT-optimized services. Far from being just a bandwidth and latency improvement, the real potential of 5G lies in the intelligent management of network resources, and in the possibility of offering new services at the network level.

This paper aims at identifying which sets of services may be offered by a 5G network, by analyzing the computing, storage, and communication services that are currently offered by 11 major IoT platform providers, as well as those that are currently not being provided due to limitations of the cloud computing paradigm.

On June 28, 2018, Alberto Monge Roffarello presented the paper End User Development in the IoT: a Semantic Approach in the Doctoral Colloquium session of the 14th International Conference on Intelligent Environments (IE '18), held in Rome, Italy.

On June 26, in the context of the 7th International Workshop on the Reliability of Intelligent Environments (WoRIE 2018) in Rome, Fabio Ballati presented his work "Hey Siri, do you understand me?" Virtual assistants and dysarthria in the poster session.

Voice-activated devices are becoming common place: people can use their voice to control smartphones, smart vacuum robots, and interact with their smart homes through virtual assistant devices like Amazon Echo or Google Home.

The spread of such voice-controlled devices is possible thanks to the increasing capabilities of natural language processing, and generally have a positive impact on the device accessibility, e.g., for people with disabilities. However, a consequence of these devices embracing voice control is that people with dysarthria or other speech impairments may be unable to control their intelligent environments, at least with proficiency.

This paper investigates to which extent people with dysarthria can use and be understood by the three most common virtual assistants, namely Siri, Google Assistant, and Amazon Alexa. Starting from the sentences in the TORGO database of dysarthric articulation, the differences between such assistants are investigated and discussed. Preliminary results show that the three virtual assistants have comparable performance, with an accuracy of the recognition in the range of 50-60%.

"User Expectations in Intelligent Environments". this was the topic addressed by Fulvio Corno in his keynote speech opening the 7th International Workshop on the Reliability of Intelligent Environments (WoRIE 2018), exploring Issues and Opportunities in the Interaction of Intelligent Users and Intelligent Environments.

The talk analyze the differences between the expectations that end-users have from Intelligent Environments (that are well capture by the IE manifestos and seminal papers), comparing them with the actual content of the research papers published in the international journals of this research community.