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
We address the problem of whether networks of mobile devices such as smart-phones or tablets can be used to perform opportunistic, best-effort, parallel computations. We designed and implemented P3-Mobile, a parallel programming system for edge-clouds of Android devices to test the feasibility of this idea. P3-Mobile comes with a programming model that supports parallel computations over peer-to-peer overlays mapped onto mobile networks. The system performs automatic load-balancing by using the overlay to discover work. We present preliminary performance results for a parallel benchmark, using up to 16 devices, and discuss their implications towards future work. Copyright © 2017 ACM.

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
We present a new and versatile sensor platform to readout the response of sensitive colorimetric films. The platform is fully self-contained and based on a switched dual-wavelength scheme. After filtering and signal processing, the system is able to provide self-referenced measures of color intensity changes in the film, while being immune to noise sources such as ambient light and fluctuations in the power source and in the optical path. By controlling the power and the switching frequency between the two wavelengths it is possible to fine tune the output gain as well as the operational range of the sensor for a particular application, thus improving the signal conditioning. The platform uses a micro-controller that complements the analog circuit used to acquire the signal. The latter pre-amplifies, filters and conditions the signal, leaving the micro-controller free to perform sensor linearization and unit conversion. By changing the sensitive film and the wavelength of the light source it is possible to use this platform for a wide range of sensing applications.

Abstract
The goal of this work is to provide (non-specialist) users with the means to seamlessly setup and monitor a Wireless Sensor-Actuator Network (WSN) without writing any code or performing subtle hardware configurations. Towards this goal, we present an architecture that allows the seamless configuration, deployment and management of applications over WSN. We explore the fact that most deployments have a common modus operandi: (a) simple data readers running on the nodes periodically gather and send data to sinks, and; (b) sinks process incoming data and, accordingly, issue actuation commands to the nodes. We argue that, given the knowledge of a platform's capabilities, its sensors and actuators and their respective programming interfaces, it is possible to fully automate the process of configuring, building, and deploying an application over a WSN. Similarly, monitoring and managing the deployment can be vastly simplified by using a middleware that supports user defined tasks that process data from the nodes, divide the WSN into regions, defined by simple boolean predicates over data, and eventually issue actuation commands on regions.

Abstract
We address the problem of providing users, namely non specialists, with out-of-the-box, programmable, Wireless Sensor-Actuator Networks (WSN). The idea is that users get a package containing a gateway and an undetermined number of nodes, pre-configured to work as a self-organized wireless mesh. Each node comes with two pre-installed components: a small operating system and a virtual machine. The user can then use a simple, domain-specific, programming language to implement periodic tasks that are compiled into byte-code, and can be sent to the nodes for execution. At the nodes, the operating system manages a task table and schedules non-preemptive tasks for execution using the virtual machine. No subtle hardware or software configuration is required from the user as these details are abstracted away by the virtual machine. We developed a full specification for a data-layer that follows the aforementioned guidelines and implemented a complete prototype, integrated in our own Publish/Subscribe middleware called SONAR. In this paper we report the first results of using the prototype as compared to using the low level programming tools provided with the hardware. We measure a small increase in both resource consumption and processing overhead suggesting that this data-layer can be used effectively in WSN, even in cases where nodes have very limited hardware resources.