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1.
HardwareX ; 11: e00283, 2022 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-35509942

RESUMEN

The ADO project proposes the development of an IoT solution that allows the digitization of the aquaculture sector, developing the basic elements (data acquisition systems, data storage system and visualization platform) in open source format. Hence, ADO makes it easier for small and medium-sized producers to obtain success stories with limited technology background and smaller economic investments. In this article we provide a comprehensive description of the platform building blocks, including the hardware elements, integration procedures and structure, and operation details of the back-end infrastructure.

2.
Sensors (Basel) ; 17(5)2017 Apr 28.
Artículo en Inglés | MEDLINE | ID: mdl-28452945

RESUMEN

In this article we present the Intelligent Industrial Internet (I3) Mote, an open hardware platform targeting industrial connectivity and sensing deployments. The I3Mote features the most advanced low-power components to tackle sensing, on-board computing and wireless/wired connectivity for demanding industrial applications. The platform has been designed to fill the gap in the industrial prototyping and early deployment market with a compact form factor, low-cost and robust industrial design. I3Mote is an advanced and compact prototyping system integrating the required components to be deployed as a product, leveraging the need for adopting industries to build their own tailored solution. This article describes the platform design, firmware and software ecosystem and characterizes its performance in terms of energy consumption.

3.
Sensors (Basel) ; 14(8): 13416-36, 2014 Jul 24.
Artículo en Inglés | MEDLINE | ID: mdl-25061839

RESUMEN

Data collection is a key scenario for the Internet of Things because it enables gathering sensor data from distributed nodes that use low-power and long-range wireless technologies to communicate in a single-hop approach. In this kind of scenario, the network is composed of one coordinator that covers a particular area and a large number of nodes, typically hundreds or thousands, that transmit data to the coordinator upon request. Considering this scenario, in this paper we experimentally validate the energy consumption of two Medium Access Control (MAC) protocols, Frame Slotted ALOHA (FSA) and Distributed Queuing (DQ). We model both protocols as a state machine and conduct experiments to measure the average energy consumption in each state and the average number of times that a node has to be in each state in order to transmit a data packet to the coordinator. The results show that FSA is more energy efficient than DQ if the number of nodes is known a priori because the number of slots per frame can be adjusted accordingly. However, in such scenarios the number of nodes cannot be easily anticipated, leading to additional packet collisions and a higher energy consumption due to retransmissions. Contrarily, DQ does not require to know the number of nodes in advance because it is able to efficiently construct an ad hoc network schedule for each collection round. This kind of a schedule ensures that there are no packet collisions during data transmission, thus leading to an energy consumption reduction above 10% compared to FSA.


Asunto(s)
Redes de Comunicación de Computadores/instrumentación , Recolección de Datos/instrumentación , Recolección de Datos/métodos , Modelos Teóricos , Teoría de Sistemas , Tecnología Inalámbrica/instrumentación
4.
Sensors (Basel) ; 14(2): 2663-82, 2014 Feb 10.
Artículo en Inglés | MEDLINE | ID: mdl-24518893

RESUMEN

Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability. Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off. Industrial sensing applications, such as those being deployed in oil refineries, have stringent requirements on data reliability and are being built using new standards. Despite the benefits of these new technologies, industrial shifts are not happening due to the enormous technology development and adoption costs and the fact that new standards are not well-known and completely understood. In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.

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