Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Hentschel, Kristian; Jacob, Dejice; Singer, Jeremy; Chalmers, Matthew

doi:10.1109/ficloud.2016.16

Cited by 40 publications

(32 citation statements)

References 15 publications

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“…In Reference [79], a smart campus sensor system was developed at the University of Glasgow in the United Kingdom. The authored use Raspberry Pi to build sensor nodes called supersensors, which are directly connected to multiple sensors and responsible for retrieving and transmitting sensor data.…”

Section: Smart Campusmentioning

confidence: 99%

“…The supersensors also behave autonomously to perform tasks such as dynamic reconfiguration and communicating with other local devices. A microservice architecture as also proposed in Reference [79], and a room temperature monitoring application based on the proposed architecture was also demonstrated in Referenece [79]. In Reference [80], a smart campus IoT framework was proposed to address issues related to energy consumption, classroom functionality, safety, and cybersecurity, and machine-learning algorithms were employed in the proposed framework.…”

Section: Smart Campusmentioning

confidence: 99%

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A Context Aware Smart Classroom Architecture for Smart Campuses

Huang

Pao

2019

Applied Sciences

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The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses to technology. In this study, an architecture for building and running context-aware smart classrooms is proposed. The proposed architecture consists of three parts including a prototype of a context-aware smart classroom, a model for technology integration, and supporting measures for the operation of smart classrooms in this architecture. The classroom prototype was designed based on our study results and a smart classroom project in Ming Chuan University (MCU). The integration model was a layered model uses Raspberry Pi in the bottom layer of the model to integrate underlying technologies and provide application interfaces to the higher layer applications for the ease of building context-aware smart classroom applications. As a result, application interfaces were implemented using Raspberry Pi based on the proposed technology integration model, and a context-aware energy-saving smart classroom application was implemented based on the proposed classroom prototype and the implemented web application interface. The result shows that, in terms of technology, the proposed architecture is feasible for building context-aware smart classrooms in smart campuses.3 of 34 as follows. The related works are reviewed in the following subsections of this section. The components of the proposed architecture, including a context-aware smart classroom prototype, a technology integration model, and the supporting measures for smart classroom operations, are described in Section 2. The implementations to prove the concepts of the proposed architecture are demonstrated in Section 3. Discussions and conclusions are given in Section 4. Background and Related WorksStudies related to this study are reviewed in this section. Studies related to different topics, including classroom environment, context-aware, energy issues, and Ming Chuan University (MCU) Smart Classroom Project, are reviewed in the following four subsections respectively. Physical Environment and Smart ClassroomsSome research works focus on taking a smart classroom as a tool to measure lecture quality. In [8], authors use sensors to retrieve features, such as noise level, CO 2 level, temperature, humidity, lecturers' voice, and lecturers' motion, in smart classrooms. Collected data are evaluated by various classification algorithms. The result shows that CO 2 level, temperature, humidity, and noise level are the main environmental factors that affect lecture quality. The relations...

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Section: Smart Campusmentioning

confidence: 99%

Section: Smart Campusmentioning

confidence: 99%

A Context Aware Smart Classroom Architecture for Smart Campuses

Huang

Pao

2019

Applied Sciences

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“…The second contextual dataset (DS2) refers to 4-dimensional contextual data collected by Raspberry Pi SANs deployed at the School of Computing Science, University of Glasgow (Hentschel et al 2016). We used four different SANs' that measured: two different room temperatures (room F121 and room S123), humidity and sound (room F121).…”

Section: Datasets and Experimental Setupmentioning

confidence: 99%

Predictive intelligence to the edge: impact on edge analytics

2017

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We rest on the edge computing paradigm where pushing processing and inference to the edge of the Internet of Things (IoT) allows the complexity of predictive analytics to be distributed into smaller pieces physically located at the source of the contextual information. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation and predictive modeling within the edge network. Our idea is based on the capability of the edge nodes to (1) monitor the evolution of the sensed time series contextual data, (2) locally determine (through prediction) whether to disseminate contextual data in the edge network or not, and (3) locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this on-line decision making, we eliminate data transfer at the edge of the network, thus saving network resources by exploiting the evolving nature of the captured contextual data. We provide comprehensive analytical, experimental and comparative evaluation of the proposed mechanism with other mechanisms found in the literature over real contextual datasets and show the benefits stemmed from its adoption in edge computing environments.

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“…In fact, the same virtualized container instance can run efficiently on both Fog and IoT/end user nodes, and in the cloud too. Hence, containers can be executed on very limited and relatively inexpensive computing equipment, like Raspberry Pi [6][7][8]. This feature offers a transversal interoperability among different networks, integrating devices with limited resources of the IoT environment/end users or Fog, as well as the cloud high-capable devices.…”

Section: Containers' Management Platforms In the Marketmentioning

confidence: 99%

“…The main reason resides on the reduction in power and costs in infrastructure, and high execution speeds in the provisioning of microservices achieved in comparison to traditional virtualization technologies such as virtual machines (VMs). On the other hand, containers are considered the first practical virtualization technology of Fog-IoT networks, due to the limited computing resources that their deployment requires compared to the rest of virtualization solutions nowadays [5][6][7][8]. However, their further expansion in cloud, fog and IoT networks critically depends on various precursor conditions, such as the design of more efficient containers' schedulers.…”

Section: Introductionmentioning

confidence: 99%

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

Prado

García-Galán

Expósito

et al. 2020

Sensors

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Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers' scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers' schedulers based on soft-computing in the dominant open-source containers' management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers' schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers' scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line.

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Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Cited by 40 publications

References 15 publications

A Context Aware Smart Classroom Architecture for Smart Campuses

A Context Aware Smart Classroom Architecture for Smart Campuses

Predictive intelligence to the edge: impact on edge analytics

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

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