Abstract
Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.

Abstract
Collections of time-series data appear in a wide variety of contexts. To gain insight into the underlying phenomenon (that the data represents), one must analyze the time-series data. Analysis can quickly become challenging for very large data (~terabytes or more) sets, and it may be infeasible to scan the entire data-set on each query due to time limits or resource constraints. To avoid this problem, one might pre-compute partial results by scanning the data-set (usually as the data arrives). However, for complex queries, where the value of a new data record depends on all of the data previously seen, this might be infeasible because incorporating a large amount of historical data into a query requires a large amount of storage. We present an approach to performing complex queries over very large data-sets in a manner that is (i) practical, meaning that a query does not require a scan of the entire data-set, and (ii) fixed-cost, meaning that the amount of storage required only depends on the time-range spanned by the entire data-set (and not the size of the data-set itself). We evaluate our approach with three different data-sets: (i) a 4-year commercial analytics data-set from a production content-delivery platform with over 15 million mobile users, (ii) an 18-year data-set from the Linux-kernel commit-history, and (iii) an 8-day data-set from Common Crawl HTTP logs. Our evaluation demonstrates the feasibility and practicality of our approach for a diverse set of complex queries on a diverse set of very large data-sets. © 2015 IEEE.

Abstract
Large scale information systems, such as public information systems for light-train/metro networks, must be able to fulfill contractualized Service Level Agreements (SLAs) in terms of end-to-end latencies and jitter, even in the presence of faults. Failure to do so has potential legal and financial implications for the software developers. Current middleware solutions have a hard time coping with these demands due, fundamentally, to a lack of adequate, simultaneous, support for fault-tolerance (FT) and real-time (RT) tasks. In this paper we present Stheno, a general purpose peer-to-peer (P2P) middleware system that builds on previous work from TAO and MEAD to provide: (a) configurable, transparent, FT support by taking advantage of the P2P layer topology awareness to efficiently implement Common Of The Shelf (COTS) replication algorithms and replica management strategies, and; (b) kernel-level resource reservation integrated with well-known threading strategies based on priorities to provide more robust support for soft real-time tasks. An evaluation of the first (unoptimized) prototype for the middleware shows that Stheno is able to match and often greatly exceed the SLA agreements provided by our target system, the light-train/metro information system developed and maintained by EFACEC, and currently deployed at multiple cities in Europe and Brazil. Copyright 2012 ACM.

Abstract
Crowd-sourcing the resources of mobile devices is a hot topic of research given the game-changing applications it may enable. In this paper we study the feasibility of using edge-clouds of mobile devices to reduce the load in traditional WiFi infrastructures for video dissemination applications. For this purpose, we designed and implemented a mobile application for video dissemination in sport venues that retrieves replays from a central server, through the access points in the WiFi infrastructure, into a smartphone. The fan's smartphones organize themselves into WiFi-Direct groups and exchange video replays whenever possible, bypassing the central server and access points. We performed a real-world experiment using the live TV feed for the Champions League game Benfica-Besiktas with the help of a group of volunteers using the application at the student's union lounge. The analysis of the logs strongly suggests that edge-clouds can significantly reduce the load in the access points at such large venues and improve quality of experience. Indeed, the edge-clouds formed were able to serve up to 80% of connected users and provide 56% of all downloads requested from within.

Abstract

Abstract
Mobile phones are a ubiquitous and preferred communication, entertainment, and information access platform. Smartphones may provide an opportunity to better assess mood and behavior and to provide intervention timely, economical, rapid and effective intervention for those with mental disorders. This is an important target because behavioral health problems are associated with many of the medical disorders most responsible for morbidity and cost. Today, psychiatrists seek for various channels of mobile technology that can reduce evaluation costs and increase accuracy and also facilitate ubiquitous longitudinal monitoring of treatment and outcome measures on patients' smartphone. Facial expression recognition is one of the active research areas in the field of psychiatry to evaluate a patient's emotional health. Smartphone technology for recognizing facial expression of emotions is still emerging and offers an open platform for the research areas such as ubiquitous intelligence and computing. In this research, we present a framework to track user's emotional engagement to videos played on a smartphone. The presented framework processes user's video recorded from the front-facing camera of a smartphone and tracks facial features to detect joyful durations induced by the played videos. We also conducted subject studies on healthy individuals to evaluate the applied approach of emotional engagement. We believe that the presented results are promising and present a valuable insight to build ubiquitous intelligent systems that can help various areas of psychiatric research. © 2013 IEEE.