Challenges of WiFi-Enabled and Solar-Powered Sensors for Smart Ports

Hanschke, Lars; Heitmann, Jan; Renner, Christian

doi:10.1145/2996884.2996887

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…It is reliable with solar power, existing or planned Wi-Fi access points. As a result, they demonstrated power compatibility of a small-sized, low-power energy machine with the Arduino MKR1000 and similar Wi-Fi nodes (Hanschke et al, 2016).…”

Section: Related Workmentioning

confidence: 98%

Veri Madenciliği Yaklaşımlarını Kullanan Yeni Bir Tarım Takip Sistemi

Albay

Doğan

2020

European Journal of Science and Technology

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Agriculture has been one of our most important needs in the world since the first ages. Nowadays, human knowledge and experience, especially in agriculture, are still lacking in achieving the most productivity. For a plant to grow close to 100% yield, multiple variables must be in optimal condition. In the Agriculture Tracking System, people are able to control the environment required for growing plants, i.e. optimal levels of their variables. Therefore, in this study, we have designed a hardware system with an Internet of Things (IoT) device and 2 sensors. The solar energy to power this hardware system has been used. Also, these sensors are a digital humidity and temperature sensor (DHT11) and Soil Moisture Sensors. Values of incoming from sensors are read with an IP Address. Moreover, these values are written to SQLite database and displayed last 5 records with bar charts. The users can save plants with optimal values. With these values, can make predictions. We have studied the effect of the sun angle on temperature and humidity in the last 5 years with months. We have compared this data with the plants added by the user and presented the most appropriate and closest months as a warning to the user. Another prediction is possible with instant records. When we researched, we saw that the condition of the plant can be categorized according to temperature and humidity. By checking the instant data, a warning message to the user according to these rates is sent by using the K-Nearest Neighbour classification algorithm. As a result, in the tests, the results have shown that this approach can increase productivity.

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Section: Related Workmentioning

confidence: 98%

Veri Madenciliği Yaklaşımlarını Kullanan Yeni Bir Tarım Takip Sistemi

Albay

Doğan

2020

European Journal of Science and Technology

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“…Not only the conceptual framework and the indicators of smart port were researched but also the technologies for smart port. The sample of the researches regarding smart port technology are as follows; Hanschke et al (2016) has implement Wifi-enabled and solar-powered sensors for smart ports at Hamburg. Carlan et al (2017) studied the Information Communication Technology (ICT) innovations in the port sector and the degree of the success achieved in each ICT innovation.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Model of Smart Port Performance and Smart Port Indicators in Thailand

Makkawan¹,

Muangpan²

2021

Journal of International Logistics and Trade

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Autonomous ports and digital ports are a modern trend of global commercial ports that are established to develop toward smart ports in many ports. Smart port indicators (SPIs) are used as important tools for measuring, encouraging, and indicating smart port performance. These are the main indicators to operate smart port management as the practical direction and port development planning are enclosed. This research aims to identify the SPIs and to develop a conceptual model of smart port performance in a case study of The Eastern Economic Corridor (EEC) in Thailand. Triangulation data are used in the data collection with three sources: the reviewed literature of five international databases in 2016–2021, participant observations, and in-depth interviews. Content analysis is utilized to analyze these data to develop a conceptual model approach. The findings of this research are shown in three main domains classified as smart port operation, smart port environment/energy, and smart port safety/security. These indicators represent 29 SPIs for developing smart port performance, which can be explained with a conceptual model. This information will exist as the foundation framework guiding Thai smart ports towards international standards of smart port efficiency.

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“…E NERGY HARVESTING via solar panels allows wireless devices to replenish their energy buffers and is thus one element towards a sustainable, maintenance-free Internet of Things (IoT) with perpetual operation, as it removes or reduces the need to switch batteries [1]. Use cases where solar power has great potential cover a wide range of domains, like smart cities [2], [3], harbors [4] and agriculture [5]. Rainforest Connection [6], for example, creates acoustic monitoring systems to detect illegal deforestation, using recycled phones powered by solar panels.…”

Section: Introductionmentioning

confidence: 99%

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

Kraemer

Palma

Bråten

et al. 2020

IEEE Internet Things J.

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For sustainable Internet of Things (IoT) systems, solar-power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large scale IoT systems. We examine which machine learning models, feature sets and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are re-trained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

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Challenges of WiFi-Enabled and Solar-Powered Sensors for Smart Ports

Cited by 7 publications

References 7 publications

Veri Madenciliği Yaklaşımlarını Kullanan Yeni Bir Tarım Takip Sistemi

Veri Madenciliği Yaklaşımlarını Kullanan Yeni Bir Tarım Takip Sistemi

A Conceptual Model of Smart Port Performance and Smart Port Indicators in Thailand

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

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