Given the prevalence of global networking and computing infrastructures (such as the Internet and the Cloud), mobile networking environments, powerful hand-held user devices, and physical-world sensing and actuation devices, the possibilities of new distributed systems, including mobile ones, have reached unprecedented levels. Such systems are dynamically composed of networked resources in the environment, which may span from the immediate neighborhood of the users – as advocated by pervasive computing – up to the entire globe – as envisioned by the Future Internet and one of its major constituents, the Internet of Things (IoT). Hence, we can now talk about truly ubiquitous computing.

 

The resulting ubiquitous systems have a number of unique – individually or in their combination – features, such as dynamicity due to volatile resources and user mobility, heterogeneity due to constituent resources developed and run independently, and context-dependence due to the highly changing characteristics of the execution environment, whether technical, physical or social. This raises the challenge of enabling such systems to emerge dynamically out of the composition of networked components or whole autonomous systems while meeting target functional and non-functional goals.

 

Relevant systems in particular rely on the physical but also social sensing and actuation capabilities of mobile devices and their users, as for instance leveraged in the context of smart spaces/cities. More specifically, the massive adoption of smart phones and other user-controlled mobile devices allows for social sensing/crowdsensing, where the user gets involved – passively or proactively – in the sensing of the environment. However, the diversity in quality and numbers of the contributed observations challenge the resource-efficient and accurate analysis of physical phenomena, while still guaranteeing privacy to the contributing users who disclose sensitive information like location.

 

Distributed systems with the above specifics further push certain problems related to the Internet and user experience to their extreme. The complexity of most Internet applications challenges even networking experts to fully diagnose and fix performance problems and anomalous network behavior, let alone most Internet users. Moreover, Quality of Experience (QoE), “the overall acceptability of an application or service as perceived subjectively by the end user” is the gold-standard to measure the performance of online services. Ideally, the detection of performance disruptions should capture when QoE degrades, not simply instances when lower-level Quality of Service (QoS) metrics degrade.

 

The above challenging context raises key research questions:

• 1. How to deal with heterogeneity and dynamicity, which create runtime uncertainty, when developing and running emergent distributed systems in the open and constantly evolving Internet and IoT environment?
• 2. How to raise human-centric social sensing/crowdsensing to a reliable and trustworthy means of capturing world phenomena?
• 3. How to enable automated diagnosis and optimization of networks and systems in the Internet and IoT environment for improving the QoE of their users?

Emergent distributed systems are systems which, due to their automated, dynamic, environment-dependent composition and execution, emerge in a possibly non-anticipated way and manifest emergent properties. In this line of research, one of MiMove’s longstanding topics is about ensuring interoperability when heterogeneous distributed components or even autonomous systems are composed together. Besides tackling the interoperability problem inside IoT systems at the middleware protocol and data semantics layers, we model and analyze the end-to-end performance of such systems by introducing reusable performance modeling patterns. Furthermore, we study the resource allocation problem for cloud based and cloud-edge based IoT systems.

Building on the widespread use of online social network services (OSNSs) and mobile applications, we investigate methods, algorithms, protocols and middleware architectures oriented towards the development of participatory systems at the urban scale, while promoting the quality of this participation – considering their contributions of a quantitative nature in a first step. In accordance with the main research objectives of MiMove, interoperability is at the heart of our research. The aim here is to overcome the heterogeneity of the contributed observations as much as of the tools used for contributions. Other considerations of our research are the introduction of efficient solutions from the point of view of resource savings as well as the quality of service provided to users.

This research focuses on developing new algorithms and systems to improve user experience online. Most Internet users are not tech-savvy and hence cannot fix performance problems and anomalous network behavior by themselves. The complexity of most Internet applications makes it hard even for networking experts to fully diagnose and fix problems. Users can’t even know whether they are getting the Internet performance that they are paying for. The long-term objective of this research theme is to automatically detect and troubleshoot network faults and performance disruptions that affect end-users. Our goal is also to help users make more informed choices when picking their Internet service and setting up their networks at home. We have focused on methods to monitor user experience (i.e., the Quality of Experience – QoE) and to diagnose impairments, in particular in home networks.