Large-Scale Internet of Things Multi-Sensor Measurement Node for Smart Grid Enhancement

Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. Thus, urgent solutions need to be found to meet this greater and greater energy demand. By using the internet of things infrastructure, we can remotely manage distribution points, receiving data that can predict any future failure points on the grid. In this work, we present the design of a fully reconfigurable wireless sensor node that can sense the smart grid environment. The proposed prototype uses a modular developed hardware platform that can be easily integrated into the smart grid concept in a scalable manner and collects data using the LoRaWAN communication protocol. The designed architecture was tested for a period of 6 months, revealing the feasibility and scalability of the system, and opening new directions in the remote failure prediction of low voltage/medium voltage switchgears on the electric grid.

Proof of concept for lightweight PUF-based authentication protocol using NodeMCU ESP8266

Wireless sensor node is the foundation for building the next generation of ubiquitous networks or the so-called internet of things (IoT). Each node is equipped with sensing, computing devices, and a radio transceiver. Each node is connected to other nodes via a wireless sensor network (WSN). Examples of WSN applications include health care monitoring, and industrial monitoring. These applications process sensitive data, which if disclosed, may lead to unwanted implications. Therefore, it is crucial to provide fundamental security services such as identification and authentication in WSN. Nevertheless, providing this security on WSN imposes a significant challenge as each node in WSN has a limited area and energy consumption. Therefore, in this study, we provide a proof of concept of a lightweight authentication protocol by using physical unclonable function (PUF) technology for resource-constrained wireless sensor nodes. The authentication protocol has been implemented on NodeMCU ESP8266 devices. A server-client protocol configuration has been used to verify the functionality of the authentication protocol. Our findings indicate that the protocol used approximately 7% of flash memory and 48% of static random-access memory (SRAM) in the sensor node during the authentication process. Hence, the proposed scheme is suitable to be used for resource-constrained IoT devices such as WSN.

Dynamic Power Management Model for a Wireless Sensor Node

Dynamic power management (DPM) is an efficient technique to design low-power and energy-efficient nodes for wireless sensor networks. This article demonstrates the stochastic behaviour of an input event arrival which is modelled with first-in first-out (FIFO) queue and a single server. An event-driven sensor node is developed based on semi-Markov model. The article investigates the factors affecting the performance of the individual sensor node with detailed analysis considering power consumption and lifetime to be the performance metrics under study. The results demonstrate the impact of the change in event arrival and the probability of change detection on the performance of the node. It is observed that (i) the number of generated events increases with the change in the average value of the distribution which affects the service time in turn resulting in a variation of the server utilization, and that (ii) the increase in the detection probability increases the power consumption decreasing the lifetime of the node.

IMAGE PROCESSING BASED SMART SERICULTURE SYSTEM USING IOT

Rearing of silkworm is highly dependent on environmental variations. To have a healthy cocoon production, it is necessary to have a proper temperature and humidity controlled house for silkworm rearing. Temperature, humidity and fresh air should be managed to get a wonderful silk product. An ideal temperature of 23°C to 28°C and humidity in between 65% to 85% is to be maintained. IoT based silkworm rearing house consists of sensors and actuators, which are interfaced with a low power controllers. The Sericulture unit can be equipped with a wireless sensor node to sense the real time Temperature and Humidity [1], also necessary actuators to control these environmental parameters. The color change in the body of the worms indicates the different stages and the light yellowish indicates that they have reached to the cocoon stage and the morphological changes in silkworm structure can be used to detect abnormal worms[2].The proposed framework introduces an Internet of Things (IoT) empowered Wireless Personal Area Network (WPAN) system. The received image is first segregated into two classes as diseased or healthy by analyzing the histogram of the background removed image based on thresholding. Again the diseased class will be sub classified into 2 diseases as either Flacherie or Pebrine by applying suitable mask for extracting worm and obtaining the histogram of the worm and analyzing it. The result will be sent to the farmer via E-mail. The proposed system could be a probable solution for productivity in silkworms.

Reconfigurable Wireless Sensor Node Remote Laboratory Platform with Cloud Connectivity

Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student’s application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor’s application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.

Open Lorawan Sensor Node Architecture for Agriculture Applications

In agriculture, it becomes more and more important to have detailed data, e.g. about weather and soil quality, not only in large scale classic crop farming applications but also for urban agriculture. This paper proposes a modular wireless sensor node that can be used in a centralized data acquisition scenario. A centralized approach, in this case multiple sensor nodes and a single gateway or a set of gateways, can be easily installed even without local infrastructure as mains supply. The sensor node integrates a LoRaWAN radio module that allows long-range wireless data transmission and low-power battery operation for several months at reasonable module costs. The developed wireless sensor node is an open system with focus on easy adaption to new sensors and applications. The proposed system is evaluated in terms of transmission range, battery runtime and sensor data accuracy.