Publications

@article{diazsanchez009,

title = {Specification and Unattended Deployment of Home Networks at the Edge of the Network},

author = {Iván Bernabé-Sánchez and Daniel Díaz-Sánchez and Mario Muñoz-Organero},

url = {https://ieeexplore.ieee.org/document/9173527},

doi = {10.1109/TCE.2020.3018543},

issn = {0098-3063},

year  = {2020},

date = {2020-08-21},

urldate = {2020-08-21},

journal = {IEEE TRANSACTIONS ON CONSUMER ELECTRONICS},

volume = {66},

issue = {4},

pages = {279 - 288},

abstract = {Consumer devices continue to expand their capabilities by connecting to digital services and other devices to form information-sharing ecosystems. This is complex and requires meeting connection requirements and minimal processing capabilities to ensure communication. The emergence of new services, and the evolution of current technologies, constantly redefine the rules of the game by opening up new possibilities and increasing competition among service providers. Paradigms such as edge computing, softwarization of physical devices, self-configuration mechanisms, definition of software as a code and interoperability between devices, define design principles to be taken into account in future service infrastructures. This work analyzes these principles and presents a programmable architecture in which services and virtual devices are instantiated in any computing infrastructure, as cloud or edge computing, upon request according to the needs specified by service providers or users. Considering that the target computing infrastructures are heterogeneous, the solution defines network elements and provides network templates to ensure it can be deployed on different infrastructures irrespectively of the vendor. A prototype has been developed and tested on a virtualized cloud-based home network relying on open source solutions.},

keywords = {Cloud computing, connected consumer devices, edge computing, fog computing, joint digital transformation, orchestrator},

pubstate = {published},

tppubtype = {article}

}

@article{diazsanchez011,

title = {Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors},

author = {Robert Simon-Sherratt and Balazs Janko and Terence Hui and William S.-Harwin and Nilanjan Dey and Daniel Díaz-Sánchez and Jin Wang and Fuqian Shi},

url = {https://doi.org/10.3390/electronics8070789

https://www.mdpi.com/2079-9292/8/7/789

https://www.mdpi.com/2079-9292/8/7/789/pdf?version=1563172692},

doi = {https://doi.org/10.3390/electronics8070789},

issn = {2079-9292},

year  = {2019},

date = {2019-07-15},

urldate = {2019-07-15},

journal = {Electronics},

volume = {8},

pages = {789},

abstract = {Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective of minimizing the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks of electrical current, creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a real-time operating system (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks’ power profile, and we discuss our application scenario, which clearly benefited from the proposal.},

keywords = {embedded, health care, joint digital transformation, low-power, task scheduler, wearable},

pubstate = {published},

tppubtype = {article}

}